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Mohammadzadeh A, Tavassoli B. Optimal linear filter design for process state and packet loss estimation in networked control systems. ISA Trans 2024; 147:79-89. [PMID: 38290864 DOI: 10.1016/j.isatra.2024.01.030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 01/25/2024] [Accepted: 01/25/2024] [Indexed: 02/01/2024]
Abstract
Considering the effect of packet losses on the behavior of networked systems, this work is concerned with estimation of the packet loss occurrences in the input channels possibly together with the system state. For this purpose, the commonly used Markov chain model of the successive packet loss occurrences is transformed to a linear recursive model in which the packet loss occurrence variables appear as new state variables. Two methods are proposed for combining the recursive packet loss model with the plant model to obtain an overall model for the whole networked control system (NCS). In the first method, a state space model of the plant is used which allows for simultaneous estimation of the packet loss occurrences and the plant state. In the second method, an input-output model of the plant is employed which allows for estimating only the packet loss occurrences. Both the zero and the hold packet loss handling strategies are considered and stability of the filters is analyzed. The proposed methods are compared with some existing results during an example to show their advantages.
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Affiliation(s)
- Amir Mohammadzadeh
- Systems and Control Department, Faculty of Electrical Engineering, K.N. Toosi university of Technology, Tehran, 1361714191, Iran.
| | - Babak Tavassoli
- Systems and Control Department, Faculty of Electrical Engineering, K.N. Toosi university of Technology, Tehran, 1361714191, Iran.
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2
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Liu S, Wang Y, Ding F, Alsaedi A, Hayat T. Joint iterative state and parameter estimation for bilinear systems with autoregressive noises via the data filtering. ISA Trans 2024; 147:337-349. [PMID: 38342649 DOI: 10.1016/j.isatra.2024.01.035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Revised: 01/29/2024] [Accepted: 01/29/2024] [Indexed: 02/13/2024]
Abstract
This paper proposes a novel iterative algorithm for the joint state and parameter estimation of bilinear state-space systems disturbed by colored noise. Estimating the states and parameters of such systems is challenging due to their nonlinearity and greater number of parameters compared to linear systems. Our method is to modify the Kalman filtering appropriately to estimate the unknown states of bilinear systems. Once the unknown states are estimated, we develop the Kalman filtering-based multi-innovation gradient-based iterative (KF-MIGI) algorithm for parameter estimation. To further improve estimation accuracy and cope with colored noises, we introduce a data filtering-based KF-MIGI algorithm that uses an adaptive filter to filter input-output data. Additionally, we compare the gradient-based iterative algorithm and the stochastic gradient algorithm. The effectiveness of the proposed algorithm is demonstrated through numerical examples.
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Affiliation(s)
- Siyu Liu
- Key Laboratory of Urban Rail Transit Intelligent Operation and Maintenance Technology & Equipment of Zhejiang Provincial, Zhejiang Normal University, 321004, Jinhua, China; Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China.
| | - Yanjiao Wang
- School of Automation, Guangdong University of Technology, Guangzhou 510006, China.
| | - Feng Ding
- Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China; School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, China.
| | - Ahmed Alsaedi
- Department of Mathematics, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Tasawar Hayat
- Department of Mathematics, King Abdulaziz University, Jeddah 21589, Saudi Arabia
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3
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Ali PR, Rehan M, Ahmed W, Basit A, Ahmed I. A novel output feedback consensus control approach for generic linear multi-agent systems under input saturation over a directed graph topology. ISA Trans 2024:S0019-0578(24)00091-0. [PMID: 38433069 DOI: 10.1016/j.isatra.2024.02.029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 02/26/2024] [Accepted: 02/26/2024] [Indexed: 03/05/2024]
Abstract
This paper considers an output feedback consensus control approach for the generic linear multi-agent systems (MASs) under input saturation over a directed graph. A region of stability-based approach has been established for dealing with the input saturation. A conventional Luenberger observer for estimating the states of followers by themselves and an advanced cooperative observer for estimating the state of leader by followers have been applied for an estimated state feedback control. The stability conditions have been derived by considering a three-term-based combined Lyapunov function. Moreover, computationally simple controller and estimator design conditions have been obtained by resorting to a decoupling approach A set of initial conditions has been investigated to achieve the leader-following consensus of MASs under the input saturation constraint. To the best of our knowledge, an output feedback consensus approach, providing a consensus region, for generic linear MASs under input saturation over directed graphs without requiring the exact state of the leader has been explored for the first time. In contrast to the existing methods, the proposed approach considers an output feedback approach (rather than the state feedback), accounts for both linear and nonlinear saturation regions, applies an estimate of the state of the leader through cooperative observer, and is based on a generalized sector condition for the saturation nonlinearity. In addition, it offers a computationally simple design solution owing to the proposed decoupling method. Simulation results are provided to validate the efficacy of the designed protocol for F-18 aircraft and unmanned ground vehicles.
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Affiliation(s)
- Paghunda Roheela Ali
- Department of Electrical Engineering, Pakistan Institute of Engineering and Applied Sciences (PIEAS), Islamabad, Pakistan.
| | - Muhammad Rehan
- Department of Electrical Engineering, Pakistan Institute of Engineering and Applied Sciences (PIEAS), Islamabad, Pakistan.
| | - Waqas Ahmed
- Department of Electrical Engineering, Pakistan Institute of Engineering and Applied Sciences (PIEAS), Islamabad, Pakistan.
| | - Abdul Basit
- Department of Electrical Engineering, Pakistan Institute of Engineering and Applied Sciences (PIEAS), Islamabad, Pakistan.
| | - Ijaz Ahmed
- Department of Electrical Engineering, Pakistan Institute of Engineering and Applied Sciences (PIEAS), Islamabad, Pakistan.
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4
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Aldana-López R, Aragüés R, Sagüés C. PLATE: A perception-latency aware estimator. ISA Transactions 2023; 142:716-730. [PMID: 37625921 DOI: 10.1016/j.isatra.2023.08.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 07/25/2023] [Accepted: 08/11/2023] [Indexed: 08/27/2023]
Abstract
Target tracking is a popular problem with many potential applications. There has been a lot of effort on improving the quality of the detection of targets using cameras through different techniques. In general, with higher computational effort applied, i.e., a longer perception-latency, a better detection accuracy is obtained. However, it is not always useful to apply the longest perception-latency allowed, particularly when the environment does not require to and when the computational resources are shared between other tasks. In this work, we propose a new Perception-LATency aware Estimator (PLATE), which uses different perception configurations in different moments of time in order to optimize a certain performance measure. This measure takes into account a perception-latency and accuracy trade-off aiming for a good compromise between quality and resource usage. Compared to other heuristic frame-skipping techniques, PLATE comes with a formal complexity and optimality analysis. The advantages of PLATE are verified by several experiments including an evaluation over a standard benchmark with real data and using state of the art deep learning object detection methods for the perception stage.
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Affiliation(s)
- Rodrigo Aldana-López
- Departamento de Informatica e Ingenieria de Sistemas (DIIS) and Instituto de Investigacion en Ingenieria de Aragon (I3A), Universidad de Zaragoza, Zaragoza 50018, Spain.
| | - Rosario Aragüés
- Departamento de Informatica e Ingenieria de Sistemas (DIIS) and Instituto de Investigacion en Ingenieria de Aragon (I3A), Universidad de Zaragoza, Zaragoza 50018, Spain.
| | - Carlos Sagüés
- Departamento de Informatica e Ingenieria de Sistemas (DIIS) and Instituto de Investigacion en Ingenieria de Aragon (I3A), Universidad de Zaragoza, Zaragoza 50018, Spain.
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5
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Wong AL, Carter L, Therrien AS. Different sensory information is used for state estimation when stationary or moving. bioRxiv 2023:2023.09.01.555979. [PMID: 37732193 PMCID: PMC10508725 DOI: 10.1101/2023.09.01.555979] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/22/2023]
Abstract
Accurate estimation of limb state is necessary for movement planning and execution. State estimation requires both feedforward and feedback information; here we focus on the latter. Prior literature has shown that integrating visual and proprioceptive feedback improve estimates of static limb position. However, differences in visual and proprioceptive feedback delays suggest that multisensory integration could be disadvantageous when the limb is moving. To investigate multisensory integration in different passive movement contexts, we compared the degree of interference created by discrepant visual or proprioceptive feedback when estimating the position of the limb either statically at the end of the movement or dynamically at movement midpoint. In the static context, we observed idiosyncratic interference: discrepant proprioceptive feedback significantly interfered with reports of the visual target location, leading to a bias of the reported position toward the proprioceptive cue. In the dynamic context, no interference was seen: participants could ignore sensory feedback from one modality and accurately reproduce the motion indicated by the other modality. We modeled feedback-based state estimation by updating the longstanding maximum likelihood estimation model of multisensory integration to account for sensory delays. Consistent with our behavioral results, the model showed that the benefit of multisensory integration was largely lost when the limb was passively moving. Together, these findings suggest that the sensory feedback used to compute a state estimate differs depending on whether the limb is stationary or moving. While the former may tend toward multimodal integration, the latter is more likely to be based on feedback from a single sensory modality.
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Affiliation(s)
- Aaron L Wong
- Moss Rehabilitation Research Institute, Elkins Park, PA, USA
- Department of Rehabilitation Medicine, Thomas Jefferson University, Philadelphia, PA, USA
| | - Luke Carter
- Moss Rehabilitation Research Institute, Elkins Park, PA, USA
| | - Amanda S Therrien
- Moss Rehabilitation Research Institute, Elkins Park, PA, USA
- Department of Rehabilitation Medicine, Thomas Jefferson University, Philadelphia, PA, USA
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6
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Sakthivel R, Kwon OM, Choi SG, Sakthivel R. Observer-based state estimation for discrete-time semi-Markovian jump neural networks with round-robin protocol against cyber attacks. Neural Netw 2023; 165:611-624. [PMID: 37364471 DOI: 10.1016/j.neunet.2023.05.046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 04/27/2023] [Accepted: 05/23/2023] [Indexed: 06/28/2023]
Abstract
This paper investigates an observer-based state estimation issue for discrete-time semi-Markovian jump neural networks with Round-Robin protocol and cyber attacks. In order to avoid the network congestion and save the communication resources, the Round-Robin protocol is used to schedule the data transmissions over the networks. Specifically, the cyber attacks are modeled as a set of random variables satisfying the Bernoulli distribution. On the basis of the Lyapunov functional and the discrete Wirtinger-based inequality technique, some sufficient conditions are established to guarantee the dissipativity performance and mean square exponential stability of the argument system. In order to compute the estimator gain parameters, a linear matrix inequality approach is utilized. Finally, two illustrative examples are provided to demonstrate the effectiveness of the proposed state estimation algorithm.
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Affiliation(s)
- Ramalingam Sakthivel
- School of Electrical Engineering, Chungbuk National University, Cheongju 28644, South Korea
| | - Oh-Min Kwon
- School of Electrical Engineering, Chungbuk National University, Cheongju 28644, South Korea.
| | - Seong-Gon Choi
- School of Information and Communication Engineering, Chungbuk National University, Cheongju 28644, South Korea
| | - Rathinasamy Sakthivel
- Department of Applied Mathematics, Bharathiar University, Coimbatore 641046, India; Department of Mathematics, Sungkyunkwan University, Suwon 440746, South Korea.
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7
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Irmak T, Pool DM, de Winkel KN, Happee R. Validating models of sensory conflict and perception for motion sickness prediction. Biol Cybern 2023; 117:185-209. [PMID: 36971844 DOI: 10.1007/s00422-023-00959-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 03/05/2023] [Indexed: 06/13/2023]
Abstract
The human motion perception system has long been linked to motion sickness through state estimation conflict terms. However, to date, the extent to which available perception models are able to predict motion sickness, or which of the employed perceptual mechanisms are of most relevance to sickness prediction, has not been studied. In this study, the subjective vertical model, the multi-sensory observer model and the probabilistic particle filter model were all validated for their ability to predict motion perception and sickness, across a large set of motion paradigms of varying complexity from literature. It was found that even though the models provided a good match for the perception paradigms studied, they could not be made to capture the full range of motion sickness observations. The resolution of the gravito-inertial ambiguity has been identified to require further attention, as key model parameters selected to match perception data did not optimally match motion sickness data. Two additional mechanisms that may enable better future predictive models of sickness have, however, been identified. Firstly, active estimation of the magnitude of gravity appears to be instrumental for predicting motion sickness induced by vertical accelerations. Secondly, the model analysis showed that the influence of the semicircular canals on the somatogravic effect may explain the differences in the dynamics observed for motion sickness induced by vertical and horizontal plane accelerations.
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Affiliation(s)
- Tugrul Irmak
- Delft University of Technology Cognitive Robotics Department, Leeghwaterstraat, Delft, The Netherlands.
| | - Daan M Pool
- Delft University of Technology Cognitive Robotics Department, Leeghwaterstraat, Delft, The Netherlands
- Control and Simulation Department, Delft University of Technology, Leeghwaterstraat, Delft, The Netherlands
| | - Ksander N de Winkel
- Delft University of Technology Cognitive Robotics Department, Leeghwaterstraat, Delft, The Netherlands
| | - Riender Happee
- Delft University of Technology Cognitive Robotics Department, Leeghwaterstraat, Delft, The Netherlands
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8
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Qu Y, Cai L. An adaptive delay-compensated filtering system and the application to path following control for unmanned surface vehicles. ISA Trans 2023; 136:548-559. [PMID: 36402598 DOI: 10.1016/j.isatra.2022.10.041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 10/06/2022] [Accepted: 10/29/2022] [Indexed: 05/16/2023]
Abstract
Given that the internal states of a system, such as position, velocity, acceleration, and other important factors, naturally obey the integral processes of a physical system in kinematics, this paper presents an adaptive noise filtering system that can reconstruct these system states at the kinematic level. This is done without using any prior knowledge of the statistical properties of measurement noises. In the proposed filtering system here, each noise-contaminated estimated state is filtered by an average filter to compensate for phase delay and amplitude distortion. Unlike existing model-based estimation methods, the dynamic equation is not explicitly used in the proposed method, and the uncertainties in the nonlinear dynamic equation can be isolated. Furthermore, this application is much more straightforward as there are no gains to be processed. To verify our proposed adaptive filtering system, it has been applied to a variable speed path-following control task for unmanned surface vehicles (USVs), where accurate system states must be known. In particular, this paper also proposes a state-constrained finite-time control framework to realize the path-following control objectives. The proposed controller here mainly consists of two parts, i.e., an online state-constrained polynomial planning function and an execution of an algebraic control law. Simulations and experiments have been conducted to validate the effectiveness and reliability of the proposed filtering system and the finite-time controller. The results show that the proposed filtering system considerably outperformed several of conventional observers such as the extended Kalman filter (EKF), the passive observer, as well as the high-order differentiator.
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Affiliation(s)
- Yang Qu
- Department of Mechanical and Aerospace Engineering, School of Engineering, The Hong Kong University of Science and Technology, Hong Kong, China.
| | - Lilong Cai
- Department of Mechanical and Aerospace Engineering, School of Engineering, The Hong Kong University of Science and Technology, Hong Kong, China.
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9
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Jalali SMS, Kalat AA. A generalized dynamic robust observer for uncertain linear time invariant descriptor systems. ISA Trans 2023; 134:226-237. [PMID: 36038364 DOI: 10.1016/j.isatra.2022.08.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/14/2019] [Revised: 08/10/2022] [Accepted: 08/10/2022] [Indexed: 06/15/2023]
Abstract
This paper presents a generalized dynamic robust observer design for uncertain linear time-invariant (LTI) singular systems. In this approach, the state equation of the singular system can consist of parametric uncertainties in three matrices namely the derivative, the system, and the input. The proposed method is according to a new parameterization in the system equations and converting it to a new descriptor model so that in the new structure, the derivative matrix is known. A generalized dynamic robust observer is suggested to estimate the state variables of the system which has more flexibility in contrast with proportional and proportional-integral observers. Also, in this method, in addition to the state variables, whose derivatives are also estimated. A sufficient condition is given in a linear matrix inequality (LMI) form to show the convergence of the observer. Numerical simulation demonstrates the efficacy of the proposed observer.
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Affiliation(s)
- Seyed Mohsen Saeed Jalali
- Department of Electrical Engineering, Technical and Vocational University (TVU), Tehran, Iran; Faculty of Electrical Engineering, Shahrood University of Technology, Shahrood, P.O. Box: 36199-95161, Iran
| | - Ali Akbarzadeh Kalat
- Faculty of Electrical Engineering, Shahrood University of Technology, Shahrood, P.O. Box: 36199-95161, Iran.
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Diaba SY, Elmusrati M. Proposed algorithm for smart grid DDoS detection based on deep learning. Neural Netw 2023; 159:175-184. [PMID: 36577364 DOI: 10.1016/j.neunet.2022.12.011] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 10/27/2022] [Accepted: 12/14/2022] [Indexed: 12/24/2022]
Abstract
The Smart Grid's objective is to increase the electric grid's dependability, security, and efficiency through extensive digital information and control technology deployment. As a result, it is necessary to apply real-time analysis and state estimation-based techniques to ensure efficient controls are implemented correctly. These systems are vulnerable to cyber-attacks, posing significant risks to the Smart Grid's overall availability due to their reliance on communication technology. Therefore, effective intrusion detection algorithms are required to mitigate such attacks. In dealing with these uncertainties, we propose a hybrid deep learning algorithm that focuses on Distributed Denial of Service attacks on the communication infrastructure of the Smart Grid. The proposed algorithm is hybridized by the Convolutional Neural Network and the Gated Recurrent Unit algorithms. Simulations are done using a benchmark cyber security dataset of the Canadian Institute of Cybersecurity Intrusion Detection System. According to the simulation results, the proposed algorithm outperforms the current intrusion detection algorithms, with an overall accuracy rate of 99.7%.
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Affiliation(s)
- Sayawu Yakubu Diaba
- Department of Telecommunication Engineering, School of Technology and Innovations, University of Vaasa, Vaasa, Finland.
| | - Mohammed Elmusrati
- Department of Telecommunication Engineering, School of Technology and Innovations, University of Vaasa, Vaasa, Finland
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Tello IFY, Wouwer AV, Coutinho D. State estimation of the time-space propagation of COVID-19 using a distributed parameter observer based on a SEIR-type model. J Process Control 2022; 118:231-241. [PMID: 36118074 PMCID: PMC9464598 DOI: 10.1016/j.jprocont.2022.08.016] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 07/27/2022] [Accepted: 08/30/2022] [Indexed: 06/15/2023]
Abstract
The real-time prediction and estimation of the spread of diseases, such as COVID-19 is of paramount importance as evidenced by the recent pandemic. This work is concerned with the distributed parameter estimation of the time-space propagation of such diseases using a diffusion-reaction epidemiological model of the susceptible-exposed-infected-recovered (SEIR) type. State estimation is based on continuous measurements of the number of infections and deaths per unit of time and of the host spatial domain. The observer design method is based on positive definite matrices to parameterize a class of Lyapunov functionals, in order to stabilize the estimation error dynamics. Thus, the stability conditions can be expressed as a set of matrix inequality constraints which can be solved numerically using sum of squares (SOS) and standard semi-definite programming (SDP) tools. The observer performance is analyzed based on a simplified case study corresponding to the situation in France in March 2020 and shows promising results.
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Affiliation(s)
- Ivan F Y Tello
- Universidad Tecnológica del Perú, Lima, Perú
- Department of Engineering, Mechatronics Section, Pontificia Universidad Católica del Perú, Lima, Perú
| | - Alain Vande Wouwer
- Systems, Estimation, Control, and Optimization (SECO), University of Mons, 7000 Mons, Belgium
| | - Daniel Coutinho
- Postgraduate Program in Engineering of Automation and Systems, Federal University of Santa Catarina, Brazil
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12
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Basit A, Tufail M, Rehan M. An adaptive gain based approach for event-triggered state estimation with unknown parameters and sensor nonlinearities over wireless sensor networks. ISA Trans 2022; 129:41-54. [PMID: 35341586 DOI: 10.1016/j.isatra.2022.02.037] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 02/18/2022] [Accepted: 02/19/2022] [Indexed: 06/14/2023]
Abstract
The distributed state and parameter estimation problem is investigated in this paper for discrete-time nonlinear systems subject to sensor nonlinearities and stochastic disturbances over a wireless sensor network. A novel architecture for distributed state estimator is introduced that incorporates adaptive coupling gains to govern the information exchange between the sensor nodes under event-triggering mechanism. The aim of this paper is to provide a scalable structure for unknown parameter identification independent of sensor networks' complexity. The boundedness of estimation error is ensured in the framework of uniformly ultimately bounded stability by developing an algebraic connectivity based criterion. The estimator gains including proposed coupling gains are then presented as solution to matrix inequalities. Finally, two simulation examples are presented to demonstrate the effectiveness of proposed estimation architecture.
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Affiliation(s)
- Abdul Basit
- Department of Electrical Engineering, Pakistan Institute of Engineering and Applied Sciences (PIEAS), Islamabad, Pakistan.
| | - Muhammad Tufail
- Department of Electrical Engineering, Pakistan Institute of Engineering and Applied Sciences (PIEAS), Islamabad, Pakistan.
| | - Muhammad Rehan
- Department of Electrical Engineering, Pakistan Institute of Engineering and Applied Sciences (PIEAS), Islamabad, Pakistan.
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13
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Xia G, Liu W, Bai H, Xue Y, Dai Y, Lei P, Zhang J. Surgical Tool Handle Vibration-Based Drilling State Recognition During Hip Fracture Fixation. Orthop Surg 2022; 14:2964-2978. [PMID: 36177881 PMCID: PMC9627077 DOI: 10.1111/os.13507] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 08/10/2022] [Accepted: 08/24/2022] [Indexed: 11/29/2022] Open
Abstract
Objectives Traditional manual drilling during hip fracture fixation can easily lead to unstable fixation and vascular damage. This study aimed to investigate a safe and easy‐to‐use robot‐assisted method to automatically drill bone and distinguish critical bone drilling states with high accuracy in real‐time for the bone hole‐making process during hip fracture fixation. Methods A bone‐drilling robotic system was designed to automatically create holes in the femoral neck. Four fresh pig femurs were drilled at the posterosuperior femoral neck using three modes: “all‐in” (AI), “in‐out‐in” (IOI), and “percutaneous fixation” (PF). A high‐frequency accelerometer captured the generated vibrations of the drill handle, which were then transferred to a personal computer using a data acquisition card. Five bone drilling states are defined, including: “drill idling,” “initial drilling,” “in the cancellous bone,” “out the femoral neck,” and “in the cortical bone.” The harmonic distribution of the vibration signal was extracted by fast Fourier transform (FFT) and used as a critical feature to identify different drilling states. To prove the difference in the harmonic distribution at different drilling states, an independent sample t‐test was used to compare the percentage of the first harmonic amplitude in the first 10 harmonics at each drilling state. A neural network classifier was trained with the frequency spectrum as the input and the drilled state as the output to distinguish the critical bone drilling states with high accuracy in real‐time. The classifier was trained and tested on four specimens to ensure that the surgical robot could accurately identify the five drilling states. Results In each specimen, the harmonic distributions of the drilling vibration at different drilling modes were significantly different (p < 0.05). The average recognition accuracies of the drilling state for the four specimens were all higher than 84%. The three defined modes were distinguished with extremely high accuracies. The recognition accuracies of “in the cancellous bone” for specimens 1 to 4 were 83.2%, 84.8%, 92.9%, and 84.7%. The recognition accuracies of “in out the femoral neck” from specimens 1 to 4 are 98.2%, 88.4%, 95.8%, and 88.8%. The recognition accuracies of “in the cortical bone” for specimens 1 to 4 were 94.6%, 80.8%, 95.5%, and 85.8%. Conclusions The proposed robot‐assisted method can automatically distinguish five critical bone‐drilling states with high accuracy in real‐time to avoid weak fixation and damage to the lateral epiphyseal artery.
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Affiliation(s)
- Guangming Xia
- Institute of Robotics and Automatic Information System, Tianjin, China
| | - Wei Liu
- Department of Orthopedic Surgery, Tianjin Medical University General Hospital, Tianjin, China.,Department of Orthopaedic Surgery, Tianjin Baodi Hospital, Tianjin, China
| | - He Bai
- Department of Orthopedic Surgery, Tianjin Medical University General Hospital, Tianjin, China
| | - Yuan Xue
- Department of Orthopedic Surgery, Tianjin Medical University General Hospital, Tianjin, China
| | - Yu Dai
- Institute of Robotics and Automatic Information System, Tianjin, China
| | - Ping Lei
- Department of Geriatrics, Tianjin Medical University General Hospital, Tianjin, China
| | - Jianxun Zhang
- Institute of Robotics and Automatic Information System, Tianjin, China
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14
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Qu D, Huang Z, Zhao Y, Song G, Yi K, Zhao X. Nonlinear state estimation by Extended Parallelotope Set-Membership Filter. ISA Trans 2022; 128:414-423. [PMID: 34933774 DOI: 10.1016/j.isatra.2021.11.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 11/24/2021] [Accepted: 11/24/2021] [Indexed: 06/14/2023]
Abstract
In this paper, we propose a state estimation method called the Extended Parallelotope Set-Membership Filter that provides a higher estimation accuracy than existing methods for discrete-time nonlinear systems. The Extended Parallelotope Set-Membership Filter is motivated by the fact that the iteration operations in existing methods generate much redundancy, and will deteriorate the accuracy of the state estimation. To account for this issue, an innovative parallelotope envelope method is proposed for the purpose of reducing the redundancy arising from the process of the noise envelope. In addition, a cofactor separation method is designed for nonlinear systems to obtain a tight envelope of the parallelotope set. Furthermore, we develop a novel parallelotope intersection method suitable for the parallelotope envelope to update the state set. The simulation results verified the effectiveness of the proposed method as well as its superiority over conventional methods in terms of both the maximum and average accuracies of the state estimation.
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Affiliation(s)
- Danyang Qu
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China; Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China; University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Zheng Huang
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China; Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China; University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Yiwen Zhao
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China; Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China; University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Guoli Song
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China; Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China; University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Kui Yi
- The Second Research Institute of Civil Aviation Administration of China, Chengdu 610041, China; Department of Automation, University of Science and Technology of China, Hefei 230027, China.
| | - Xingang Zhao
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China; Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China; University of Chinese Academy of Sciences, Beijing 100049, China.
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15
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Tang X, Liu Z. Sliding mode observer-based adaptive control of uncertain singular systems with unknown time-varying delay and nonlinear input. ISA Trans 2022; 128:133-143. [PMID: 34625222 DOI: 10.1016/j.isatra.2021.09.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 09/17/2021] [Accepted: 09/17/2021] [Indexed: 06/13/2023]
Abstract
A novel adaptive sliding mode observer-based control strategy is put forward for a type of uncertain singular systems subject to unknown state delay, input nonlinearity and uncertain perturbation in this article. Firstly, the unmeasured state is reconstructed by a particular observer without any inputs, from which a novel linear switching surface is provided. Subsequently, performance analysis of the resultant system on the switching surface is ensured under a new admissibility criterion. An associated adaptive control signal is synthesized to ensure that the established switching surface can be attained in finite moment. Finally, three numerical simulations are conducted to confirm the validity and superiority of the specified method.
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Affiliation(s)
- Xiaoliang Tang
- School of Automation, Qingdao University, Qingdao 266071, China; Shandong Key Laboratory of Industrial Control Technology, Qingdao University, Qingdao 266071, China
| | - Zhen Liu
- School of Automation, Qingdao University, Qingdao 266071, China; Shandong Key Laboratory of Industrial Control Technology, Qingdao University, Qingdao 266071, China.
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16
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Jiang T, Wang J, He Y, Wang Y. Design of the modified fractional central difference Kalman filters under stochastic colored noises. ISA Trans 2022; 127:487-500. [PMID: 34521507 DOI: 10.1016/j.isatra.2021.08.044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Revised: 08/27/2021] [Accepted: 08/27/2021] [Indexed: 06/13/2023]
Abstract
For state estimation of discrete nonlinear fractional stochastic systems, this study presents two innovative modified fractional central difference Kalman filters. We consider a complicated scenario where the process noise or measurement noise in the system become colored noise. Firstly, the nonlinear function is linearized by utilizing the Stirling polynomial interpolation formula. Thus there is no need to calculate the Jacobi matrix for both algorithms, which means very few application limitations. Then, based on the augmented-state method, we develop an augmented state fractional central difference Kalman filter under the scenario of colored process noise. Afterwards, a state estimation algorithm for handling stochastic systems containing colored measurement noise is put forward by using the measurement expansion method. Finally, to perform the superiority of the developed algorithms, several simulations are carried out. As well, the algorithms derived in this paper are contrasted with the original fractional central difference Kalman filter and three other algorithms. Notably, a simulation with engineering significance for the state-of-charge estimation for lithium-ion batteries is also introduced, Aside from the commonly used numerical simulation. The results verify the superiority of the developed algorithms in sense of estimation accuracy and real-time performance.
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Affiliation(s)
- Tiantian Jiang
- Department of Automation, University of Science and Technology of China, Hefei, 230026, China
| | - Jianli Wang
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, 130033, China
| | - Yuli He
- Department of Automation, University of Science and Technology of China, Hefei, 230026, China
| | - Yong Wang
- Department of Automation, University of Science and Technology of China, Hefei, 230026, China.
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17
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Guo H, Sun J, Pang ZH. Stealthy false data injection attacks with resource constraints against multi-sensor estimation systems. ISA Trans 2022; 127:32-40. [PMID: 35292173 DOI: 10.1016/j.isatra.2022.02.045] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 02/16/2022] [Accepted: 02/24/2022] [Indexed: 06/14/2023]
Abstract
This paper mainly investigates how to maximally degrade estimation performance of a cyber-physical system under limited resource. A stealthy false data injection (FDI) attack scheme is proposed to only attack partial sensor channels of a multi-sensor estimation system. The attack stealthiness condition and the compromised estimation error covariance are respectively derived, and then the stealthy attack problem is formed as a constrained optimization problem. An explicit solution of the optimal attack strategy is given and proven. Furthermore, the relationship between the compromised estimation error covariance and the attacked sensor is analyzed, and then the sensor selection principle is derived to decide which sensor channel should be attacked. Finally, two numerical simulation examples are provided to confirm the theoretical analysis results.
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Affiliation(s)
- Haibin Guo
- State Key Lab of Intelligent Control and Decision of Complex Systems, School of Automation, Beijing Institute of Technology, Beijing 100081, China
| | - Jian Sun
- State Key Lab of Intelligent Control and Decision of Complex Systems, School of Automation, Beijing Institute of Technology, Beijing 100081, China; Beijing Institute of Technology Chongqing Innovation Center, Chongqing 401120, China
| | - Zhong-Hua Pang
- Key Lab of Fieldbus Technology and Automation of Beijing, North China University of Technology, Beijing 100144, China.
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18
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Graybill PP, Gluckman BJ, Kiani M. Optimization of an unscented Kalman filter for an embedded platform. Comput Biol Med 2022; 146:105557. [PMID: 35598350 DOI: 10.1016/j.compbiomed.2022.105557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 04/08/2022] [Accepted: 04/22/2022] [Indexed: 02/07/2023]
Abstract
The unscented Kalman filter (UKF) is finding increased application in biological fields. While realizing a complex UKF system in a low-power embedded platform offers many potential benefits including wearability, it also poses significant design challenges. Here we present a method for optimizing a UKF system for realization in an embedded platform. The method seeks to minimize both computation time and error in UKF state reconstruction and forecasting. As a case study, we applied the method to a model for the rat sleep-wake regulatory system in which 432 variants of the UKF over six different variables are considered. The optimization method is divided into three stages that assess computation time, state forecast error, and state reconstruction error. We apply a cost function to variants that pass all three stages to identify a variant that computes 27 times faster than the reference variant and maintains required levels of state estimation and forecasting accuracy. We draw the following insights: 1) process noise provides leeway for simplifying the model and its integration in ways that speed computation time while maintaining state forecasting accuracy, 2) the assimilation of observed data during the UKF correction step provides leeway for simplifying the UKF structure in ways that speed computation time while maintaining state reconstruction accuracy, and 3) the optimization process can be accelerated by decoupling variables that directly impact the underlying model from variables that impact the UKF structure.
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19
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Liu Z, Li Y, Wu Y, He S. Formation control of nonholonomic unmanned ground vehicles via unscented Kalman filter-based sensor fusion approach. ISA Trans 2022; 125:60-71. [PMID: 34353617 DOI: 10.1016/j.isatra.2021.07.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/27/2020] [Revised: 07/06/2021] [Accepted: 07/07/2021] [Indexed: 06/13/2023]
Abstract
This paper investigates the formation control of nonholonomic unmanned ground vehicles via unscented Kalman filter-based sensor fusion approach. According to the kinematic model of single unmanned ground vehicle, the formation model of multiple unmanned ground vehicles is established. Note that the physical leader is considered instead of a virtual leader, which is more realistic. The formation control problem is converted to the stability problem of an error dynamic system. An asymptotic stability condition of the error dynamic system is derived by designing an appropriate Lyapunov function. The leader-following formation is well formed through designing effective control vectors and utilizing unscented Kalman filter-based state estimation algorithm for each follower. Some simulation examples are provided to verify the effectiveness of the proposed formation control algorithm.
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Affiliation(s)
- Zhengyuan Liu
- Logistics Engineering, Army Logistics University, 401331, China.
| | - Yanzhou Li
- School of Automation, Guangdong University of Technology, Guangzhou, 510006, Guangdong, China.
| | - Yuanqing Wu
- School of Automation, Guangdong University of Technology, Guangzhou, 510006, Guangdong, China.
| | - Shenghuang He
- School of Automation, Guangdong University of Technology, Guangzhou, 510006, Guangdong, China.
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20
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Azimian B, Biswas RS, Moshtagh S, Pal A, Tong L, Dasarathy G. State and Topology Estimation for Unobservable Distribution Systems using Deep Neural Networks. IEEE Trans Instrum Meas 2022; 71:9003514. [PMID: 36277673 PMCID: PMC9585895 DOI: 10.1109/tim.2022.3167722] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Time-synchronized state estimation for reconfigurable distribution networks is challenging because of limited real-time observability. This paper addresses this challenge by formulating a deep learning (DL)-based approach for topology identification (TI) and unbalanced three-phase distribution system state estimation (DSSE). Two deep neural networks (DNNs) are trained for time-synchronized DNN-based TI and DSSE, respectively, for systems that are incompletely observed by synchrophasor measurement devices (SMDs) in real-time. A data-driven approach for judicious SMD placement to facilitate reliable TI and DSSE is also provided. Robustness of the proposed methodology is demonstrated by considering non-Gaussian noise in the SMD measurements. A comparison of the DNN-based DSSE with more conventional approaches indicates that the DL-based approach gives better accuracy with smaller number of SMDs.
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Affiliation(s)
- Behrouz Azimian
- School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ, 85287, USA
| | - Reetam Sen Biswas
- School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ, 85287, USA
| | - Shiva Moshtagh
- School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ, 85287, USA
| | - Anamitra Pal
- School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ, 85287, USA
| | - Lang Tong
- School of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14850, USA
| | - Gautam Dasarathy
- School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ, 85287, USA
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21
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Czyżniewski M, Łangowski R. A robust sliding mode observer for non-linear uncertain biochemical systems. ISA Trans 2022; 123:25-45. [PMID: 34119305 DOI: 10.1016/j.isatra.2021.05.040] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Revised: 05/28/2021] [Accepted: 05/30/2021] [Indexed: 06/12/2023]
Abstract
A problem of state estimation for a certain class of non-linear uncertain systems has been addressed in this paper. In particular, a sliding mode observer has been derived to produce robust and stable estimates of the state variables. The stability and robustness of the proposed sliding mode observer have been investigated under parametric and unstructured uncertainty in the system dynamics. In order to ensure an unambiguous non-linear state (coordinates) transformation, the appropriate system model for the observer synthesis has been devised and analysed. The stability analysis of dynamics of estimation error has been carried out, based on the Lyapunov stability theory in relation to Lipschitz assumptions for non-linear functions. In order to validate the performance of the devised observer, it has been applied to the model of a continuous stirred tank reactor (bioreactor). The promising simulation results have been obtained and they demonstrate the high effectiveness of the devised approach.
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Affiliation(s)
- Mateusz Czyżniewski
- Department of Electrical Engineering, Control Systems and Informatics, Gdańsk University of Technology, G. Narutowicza 11/12, 80-233 Gdańsk, Poland
| | - Rafał Łangowski
- Department of Electrical Engineering, Control Systems and Informatics, Gdańsk University of Technology, G. Narutowicza 11/12, 80-233 Gdańsk, Poland.
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22
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Mankad J, Natarajan B, Srinivasan B. Integrated approach for optimal sensor placement and state estimation: A case study on water distribution networks. ISA Trans 2022; 123:272-285. [PMID: 34130860 DOI: 10.1016/j.isatra.2021.06.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Revised: 06/03/2021] [Accepted: 06/03/2021] [Indexed: 06/12/2023]
Abstract
The objective of the design and operation of any water distribution network (WDN) includes meeting the desired demand at sufficient pressure at all nodes. However, this requires situational awareness; in other words, the knowledge of system state variables such as pressure and flow throughout the network. In this work, a hybrid approach is developed for sensor placement (SP) and state estimation (SE) that exploits the underlying correlation structure in the data, along with the principles governing the flow through circular pipes. The problem of SP in WDN is addressed since measuring the state variables throughout the network is not practical. The problem of SE that maps to a matrix completion problem under certain physical and logical constraints is solved later. The completed matrix represents the state of WDN at any given time. Benchmark networks used in literature were used to evaluate the proposed approach. The mean absolute percentage error (MAPE) of less than 5% was obtained while estimating the head available at nodes. The knowledge of the states in the entire network could help operate the network adaptively.
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Affiliation(s)
- Jaivik Mankad
- Indian Institute of Technology Gandhinagar, Gandhinagar, Gujarat, India
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23
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Share Pasand MM, Ahmadi AA. Performance evaluation and simulation of cubic observers. ISA Trans 2022; 122:172-181. [PMID: 33941377 DOI: 10.1016/j.isatra.2021.04.032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Revised: 04/25/2021] [Accepted: 04/25/2021] [Indexed: 06/12/2023]
Abstract
This paper investigates the performance of cubic observers in state estimation of linear systems. In particular, the proposed observer yields a smaller estimation error norm in comparison with a linear one. It is then shown that cubic observers can be designed to perform similar to linear observers in presence of disturbances and delays. It also compares a cubic observer with a nonlinear extended observer in a simulation example.
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Affiliation(s)
- Mohammad Mahdi Share Pasand
- Department of Electrical Engineering, Faculty of Technology and Engineering, Standard Research Institute, Alborz, PO Box 31585-163, Iran.
| | - Ali Akbar Ahmadi
- Department of Electrical and Computer Engineering, Faculty of Engineering, Kharazmi University, Tehran, PO Box 15719-14911, Iran.
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24
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Pan Z, Luan X, Liu F. Confidence set-membership state estimation for LPV systems with inexact scheduling variables. ISA Trans 2022; 122:38-48. [PMID: 33926723 DOI: 10.1016/j.isatra.2021.04.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/24/2021] [Revised: 03/28/2021] [Accepted: 04/16/2021] [Indexed: 06/12/2023]
Abstract
In this paper, a confidence set-membership state estimator is proposed for a class of polytopic linear parameter varying (LPV) systems with inexact scheduling variables. The set-bounded and Gaussian uncertainties are considered simultaneously in the process disturbances and measurement noises. The purpose of the proposed estimator is to achieve a confidence set of the state with given confidence level. Based on the polytopic LPV uncertain enclosure model, the set-bounded/Gaussian uncertainties of the state are given by using the worst case strategy. The size of the confidence set is minimized to get the optimal gain for the estimator. Meanwhile, the constrained zonotope is adopted to represent set-bounded uncertainties for more accurate results. Finally, a vehicle example is given to illustrate the effectiveness of proposed methods.
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Affiliation(s)
- Zhichao Pan
- Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Institute of Automation, Jiangnan University, Wuxi, 214122, China.
| | - Xiaoli Luan
- Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Institute of Automation, Jiangnan University, Wuxi, 214122, China.
| | - Fei Liu
- Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Institute of Automation, Jiangnan University, Wuxi, 214122, China.
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25
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Jin Y, Xie G, Li Y, Shang L, Hei X, Ji W, Han N, Wang B. Multi-model train state estimation based on multi-sensor parallel fusion filtering. Accid Anal Prev 2022; 165:106506. [PMID: 34890921 DOI: 10.1016/j.aap.2021.106506] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Revised: 11/01/2021] [Accepted: 11/21/2021] [Indexed: 06/13/2023]
Abstract
Accurately determining a train's state is essential for passenger safety, operation efficiency, and maintenance. However, the actual operation state of a train is composed of a variety of modes and is disturbed by several known or unknown factors, for which an accurate estimator is required. Hence, in this paper, a train multi-mode model considering the actual operation environment is established, and a train state estimation method based on multi-sensor parallel fusion filter is proposed. In the parallel fusion filter, the current mode of train is determined by the proposed sliding window error and voting mechanism, and the global filter are constituted by the local filters, which are fused by linear-weighted summation. The simulation results demonstrate the effectiveness of our method in estimating the train's state. It is worth noting that even if monitoring data are missing or are abnormal, the state estimation accuracy of the proposed technique still meets the requirements of a real system, and the effectiveness and robustness of the method can be verified.
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Affiliation(s)
- Yongze Jin
- Shannxi Key Laboratory of Complex System Control and Intelligent Information Processing, Xi'an University of Technology, Xi'an 710048, China; China Academy of Railway Sciences Signal & Communication Research Institute, Beijing 100081, China
| | - Guo Xie
- Shannxi Key Laboratory of Complex System Control and Intelligent Information Processing, Xi'an University of Technology, Xi'an 710048, China; China Academy of Railway Sciences Signal & Communication Research Institute, Beijing 100081, China.
| | - Yankai Li
- Shannxi Key Laboratory of Complex System Control and Intelligent Information Processing, Xi'an University of Technology, Xi'an 710048, China; China Academy of Railway Sciences Signal & Communication Research Institute, Beijing 100081, China
| | - Linyu Shang
- Shannxi Key Laboratory of Complex System Control and Intelligent Information Processing, Xi'an University of Technology, Xi'an 710048, China; China Academy of Railway Sciences Signal & Communication Research Institute, Beijing 100081, China
| | - Xinhong Hei
- Shannxi Key Laboratory of Complex System Control and Intelligent Information Processing, Xi'an University of Technology, Xi'an 710048, China; China Academy of Railway Sciences Signal & Communication Research Institute, Beijing 100081, China
| | - Wenjiang Ji
- Shannxi Key Laboratory of Complex System Control and Intelligent Information Processing, Xi'an University of Technology, Xi'an 710048, China; China Academy of Railway Sciences Signal & Communication Research Institute, Beijing 100081, China
| | - Ning Han
- Shannxi Key Laboratory of Complex System Control and Intelligent Information Processing, Xi'an University of Technology, Xi'an 710048, China; China Academy of Railway Sciences Signal & Communication Research Institute, Beijing 100081, China
| | - Bo Wang
- Shannxi Key Laboratory of Complex System Control and Intelligent Information Processing, Xi'an University of Technology, Xi'an 710048, China; China Academy of Railway Sciences Signal & Communication Research Institute, Beijing 100081, China
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26
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Tomy A, Razzanelli M, Di Lauro F, Rus D, Della Santina C. Estimating the state of epidemics spreading with graph neural networks. Nonlinear Dyn 2022; 109:249-263. [PMID: 35079201 PMCID: PMC8777184 DOI: 10.1007/s11071-021-07160-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Accepted: 10/31/2021] [Indexed: 06/14/2023]
Abstract
When an epidemic spreads into a population, it is often impractical or impossible to continuously monitor all subjects involved. As an alternative, we propose using algorithmic solutions that can infer the state of the whole population from a limited number of measures. We analyze the capability of deep neural networks to solve this challenging task. We base our proposed architecture on Graph Convolutional Neural Networks. As such, it can reason on the effect of the underlying social network structure, which is recognized as the main component in spreading an epidemic. The proposed architecture can reconstruct the entire state with accuracy above 70%, as proven by two scenarios modeled on the CoVid-19 pandemic. The first is a generic homogeneous population, and the second is a toy model of the Boston metropolitan area. Note that no retraining of the architecture is necessary when changing the model.
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Affiliation(s)
- Abhishek Tomy
- Centre of Innovation in Telecommunications and Integration of services, Inria Grenoble - Rhône-Alpes, Inovallée, France
| | | | | | - Daniela Rus
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology (MIT), Cambridge, MA United States
| | - Cosimo Della Santina
- Cognitive Robotics Department, Faculty of Mechanical, Maritime and Materials Engineering, TU Delft, Delft, Netherlands
- Institute of Robotics and Mechatronics, German Aerospace Center (DLR), Oberpfaffenhofen, Germany
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27
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Péni T, Szederkényi G. Convex output feedback model predictive control for mitigation of COVID-19 pandemic. Annu Rev Control 2021; 52:543-553. [PMID: 34720662 PMCID: PMC8549322 DOI: 10.1016/j.arcontrol.2021.10.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 07/07/2021] [Accepted: 10/05/2021] [Indexed: 06/13/2023]
Abstract
In this paper, a model predictive control approach is proposed for epidemic mitigation. The disease spreading dynamics is described by an 8-compartment smooth nonlinear model of the COVID-19 pandemic in Hungary known from the literature, where the manipulable control input is the stringency of the introduced non-pharmaceutical measures. It is assumed that only the number of hospitalized people is measured on-line, and the other state variables are computed using a state observer which is based on the dynamic inversion of a linear sub-system of the model. The objective function contains a measure of the direct harmful consequences of the restrictions, and the constraints refer to input bounds and to the capacity of the healthcare system. By exploiting the special properties of the model, the nonlinear optimization problem required by the control design is reformulated to convex tasks, allowing a computationally efficient solution. Two approaches are proposed: the first finds a suboptimal solution by geometric programming, while the second one further simplifies the problem and transforms it to a linear programming task. Simulations show that both suboptimal solutions fulfill the design specifications even in the presence of parameter uncertainties.
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Affiliation(s)
- T Péni
- Institute for Computer Science and Control (SZTAKI), Eötvös Lóránd Research Network (ELKH), H-1111, Kende u. 13-17., Budapest, Hungary
| | - G Szederkényi
- Pázmány Péter Catholic University, H-1083 Práter u. 50/a, Budapest, Hungary
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28
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Destro F, García Muñoz S, Bezzo F, Barolo M. Powder composition monitoring in continuous pharmaceutical solid-dosage form manufacturing using state estimation - Proof of concept. Int J Pharm 2021; 605:120808. [PMID: 34144142 DOI: 10.1016/j.ijpharm.2021.120808] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Revised: 05/25/2021] [Accepted: 06/13/2021] [Indexed: 12/18/2022]
Abstract
In continuous solid-dosage form manufacturing, the powder feeding system is responsible for supplying downstream the correct formulation of the drug product ingredients. The composition of the powder delivered by the feeding system is inferred from the measurements of powder mass flow from the system feeders. The mass flows are, in turn, inferred from the loss in weight measured in the feeder hoppers. Most loss-in-weight feeders post-process the mass flow signal to deliver a smoothed value to the user. However, such estimated mass flows can exhibit a low signal-to-noise ratio. As the feeders are critical elements of the control strategy of the manufacturing line, better instantaneous estimates of mass flow are desirable for improving the quality assurance. In this study, we propose a model-based approach for monitoring the composition of the powder fed to a continuous solid-dosage line. The monitoring system is based on a moving-horizon state estimator, which carries out model-based reconciliation of the feeder mass measurements, thus enabling accurate composition estimation of the powder mixture. Experimental datasets from a direct compression line are used to validate the methodology. Results demonstrate improvement with respect to current industrial solutions.
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29
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Fernández MC, Pantano MN, Rodriguez L, Scaglia G. State estimation and nonlinear tracking control simulation approach. Application to a bioethanol production system. Bioprocess Biosyst Eng 2021; 44:1755-1768. [PMID: 33993385 DOI: 10.1007/s00449-021-02558-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Revised: 04/20/2020] [Accepted: 05/08/2020] [Indexed: 11/24/2022]
Abstract
Tracking control of specific variables is key to achieve a proper fermentation. This paper analyzes a fed-batch bioethanol production process. For this system, a controller design based on linear algebra is proposed. Moreover, to achieve a reliable control, on-line monitoring of certain variables is needed. In this sense, for unmeasurable variables, state estimators based on Gaussian processes are designed. Cell, ethanol and glycerol concentrations are predicted with only substrates measurement. Simulation results when the controller and estimators are coupled, are shown. Furthermore, the algorithms were tested with parametric uncertainties and disturbances in the control action, and are compared, in all cases, with neural networks estimators (previous work). Bayesian estimators show a performance improvement, which is reflected in a decrease of the total error. Proposed techniques give reliable monitoring and control tools, with a low computational and economic cost, and less mathematical complexity than neural network estimators.
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Affiliation(s)
- M Cecilia Fernández
- Instituto de Ingeniería Química, Universidad Nacional de San Juan (UNSJ), CONICET, Av. Lib. San Martín Oeste 1109, J5400ARL, San Juan, Argentina.
| | - M Nadia Pantano
- Instituto de Ingeniería Química, Universidad Nacional de San Juan (UNSJ), CONICET, Av. Lib. San Martín Oeste 1109, J5400ARL, San Juan, Argentina
| | - Leandro Rodriguez
- Instituto de Ingeniería Química, Universidad Nacional de San Juan (UNSJ), CONICET, Av. Lib. San Martín Oeste 1109, J5400ARL, San Juan, Argentina
| | - Gustavo Scaglia
- Instituto de Ingeniería Química, Universidad Nacional de San Juan (UNSJ), CONICET, Av. Lib. San Martín Oeste 1109, J5400ARL, San Juan, Argentina
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30
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Lin L, Liu Z. State-estimation-based adaptive sliding mode control of uncertain switched systems: A novel linear sliding manifold approach. ISA Trans 2021; 111:47-56. [PMID: 33189305 DOI: 10.1016/j.isatra.2020.11.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2019] [Revised: 09/29/2020] [Accepted: 11/04/2020] [Indexed: 06/11/2023]
Abstract
A new sliding mode observer design scheme is developed with a novel linear sliding manifold for the exponential stabilization problem of a class of uncertain switched systems in this note. Firstly, the linear sliding manifold is constructed based on a modified observer, through which a new technical approach of the underlying system analysis is exploited to devise a novel exponential stability criteria. Moreover, an associated adaptive switching controller is presented, by which the arrival condition of the designated sliding manifold is fulfilled. Ultimately, illustrative examples are conducted to confirm the feasibility of the proposed scheme.
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Affiliation(s)
- Luxin Lin
- School of Automation, Qingdao University, Qingdao 266071, China; Shandong Key Laboratory of Industrial Control Technology, Qingdao University, Qingdao 266071, China
| | - Zhen Liu
- School of Automation, Qingdao University, Qingdao 266071, China; Shandong Key Laboratory of Industrial Control Technology, Qingdao University, Qingdao 266071, China.
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31
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Honarmand-Shazilehei F, Pariz N, Naghibi Sistani MB. Sensor fault detection in a class of nonlinear systems using modal Kalman filter. ISA Trans 2020; 107:214-223. [PMID: 32829889 DOI: 10.1016/j.isatra.2020.08.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2018] [Revised: 08/04/2020] [Accepted: 08/05/2020] [Indexed: 06/11/2023]
Abstract
Kalman filter and its different variants are commonly used as optimal methods for fault detection in various types of system components. In this paper, a newly introduced type of aforementioned filters, called modal Kalman filter, is extended and utilized in order to estimate the states of nonlinear systems, for sensor fault detection purposes, in a class of nonlinear certain systems. This method, in contrast to the extended Kalman filter, which employs only the linear term of Taylor expansion, retains higher-order terms; as a result, the estimation error will reduce accordingly. Practicality and effectivity of this method, and its superiority over Kalman filter, in terms of accuracy and promptness of sensor fault detection, are also verified with simulation results.
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Affiliation(s)
| | - Naser Pariz
- Department of Electrical Engineering, Ferdowsi University of Mashhad, P.O. Box 91775-1111, Mashhad, Iran.
| | - Mohammad B Naghibi Sistani
- Department of Electrical Engineering, Ferdowsi University of Mashhad, P.O. Box 91775-1111, Mashhad, Iran.
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32
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LeBlanc B, Hernandez EM, McGinnis RS, Gurchiek RD. Continuous estimation of ground reaction force during long distance running within a fatigue monitoring framework: A Kalman filter-based model-data fusion approach. J Biomech 2020; 115:110130. [PMID: 33257007 DOI: 10.1016/j.jbiomech.2020.110130] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Revised: 09/25/2020] [Accepted: 11/09/2020] [Indexed: 10/23/2022]
Abstract
Estimation of ground reaction forces in runners has been limited to laboratory environments by means of instrumented treadmills, in-ground force plates and optoelectronic systems. Recent advances in estimation techniques using wearable sensors for kinematic analysis and sports performance could enable estimation outside the laboratory. This paper proposes a state-input-parameter estimation framework to continuously estimate the vertical ground reaction force waveform during running. By modeling a runner as a single degree of freedom mass-spring-damper with acceleration measurements at the sacrum a state-space formulation can be applied using Newtonian methods. A dual-Kalman filter is employed to estimate the unmeasured system input which feeds through to an unscented Kalman filter to estimate system dynamics and unknown model parameters (e.g. spring stiffness). For validation, 14 subjects performed three one-minute running trials at three different speeds (self-selected slow, comfortable, and fast) on a pressure-sensor-instrumented treadmill. The estimated vertical ground reaction force waveform parameters; peak vertical ground reaction force (RMSE=6.1-7.2%,ρ=0.95-0.97), vertical impulse (RMSE=8.5-13.0%,ρ=0.50-0.60), loading rate (RMSE=24.6-39.4%,ρ=0.85-0.93), and cadence RMSE<1%,ρ=1.00 were compared against the instrumented treadmill measurements. The proposed state-input-parameter estimation framework could monitor personalized vertical ground reaction force metrics for potential biofeedback applications. The feedback mechanism could provide information about the vertical ground reaction force characteristics to the runner as they are running to provide knowledge of both desirable and undesirable loading characteristics experienced.
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Affiliation(s)
- Benjamin LeBlanc
- College of Engineering and Mathematical Sciences, Department of Civil and Environmental Engineering, University of Vermont, Burlington, VT 05405, USA.
| | - Eric M Hernandez
- College of Engineering and Mathematical Sciences, Department of Civil and Environmental Engineering, University of Vermont, Burlington, VT 05405, USA
| | - Ryan S McGinnis
- College of Engineering and Mathematical Sciences, Department of Electrical and Biomedical Engineering, University of Vermont, Burlington, VT 05405, USA
| | - Reed D Gurchiek
- College of Engineering and Mathematical Sciences, Department of Electrical and Biomedical Engineering, University of Vermont, Burlington, VT 05405, USA
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33
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Allak E, Brommer C, Dallenbach D, Weiss S. AMADEE-18: Vision-Based Unmanned Aerial Vehicle Navigation for Analog Mars Mission (AVI-NAV). Astrobiology 2020; 20:1321-1337. [PMID: 33179969 DOI: 10.1089/ast.2019.2036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
As a part of the AMADEE-18 analog Mars mission, designed to study challenges associated with human-based exploration of the Red Planet, we focused our team efforts on testing means to localize an unmanned aerial vehicle (UAV) on Mars. Robot helicopters, such as the one selected for a technology demonstration as a part of NASA's Mars 2020 mission, are small and their performance is computationally limited. An essential aspect of navigation and path planning of an autonomous helicopter is accurate localization of the robot. In the absence of a global positioning system, a computationally efficient localization technology that can be applied on Mars is visual-inertial odometry (VIO). The AMADEE-18 mission provided an opportunity to test the feasibility of a state-of-the-art VIO algorithm and the camera in a Mars-like analog environment. The flight datasets included different terrain structures that challenged the functionality of VIO algorithms. The experiment has yielded valuable insights into the desired surface structure, texture, and mission times for surface relative navigation of UAV on Mars.
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Affiliation(s)
- Eren Allak
- Control of Networked Systems Group, Department of Smart Systems Technologies, University of Klagenfurt, Klagenfurt, Austria
| | - Christian Brommer
- Control of Networked Systems Group, Department of Smart Systems Technologies, University of Klagenfurt, Klagenfurt, Austria
| | - Diego Dallenbach
- Control of Networked Systems Group, Department of Smart Systems Technologies, University of Klagenfurt, Klagenfurt, Austria
| | - Stephan Weiss
- Control of Networked Systems Group, Department of Smart Systems Technologies, University of Klagenfurt, Klagenfurt, Austria
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34
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Liu S, Wang Z, Chen Y, Wei G. Dynamic event-based state estimation for delayed artificial neural networks with multiplicative noises: A gain-scheduled approach. Neural Netw 2020; 132:211-219. [PMID: 32916602 DOI: 10.1016/j.neunet.2020.08.023] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Revised: 08/12/2020] [Accepted: 08/24/2020] [Indexed: 11/24/2022]
Abstract
This study is concerned with the state estimation issue for a kind of delayed artificial neural networks with multiplicative noises. The occurrence of the time delay is in a random way that is modeled by a Bernoulli distributed stochastic variable whose occurrence probability is time-varying and confined within a given interval. A gain-scheduled approach is proposed for the estimator design to accommodate the time-varying nature of the occurrence probability. For the sake of utilizing the communication resource as efficiently as possible, a dynamic event triggering mechanism is put forward to orchestrate the data delivery from the sensor to the estimator. Sufficient conditions are established to ensure that, in the simultaneous presence of the external noises, the randomly occurring time delays with time-varying occurrence probability as well as the dynamic event triggering communication protocol, the estimation error is exponentially ultimately bounded in the mean square. Moreover, the estimator gain matrices are explicitly calculated in terms of the solution to certain easy-to-solve matrix inequalities. Simulation examples are provided to show the validity of the proposed state estimation method.
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Affiliation(s)
- Shuai Liu
- College of Science, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Zidong Wang
- Department of Computer Science, Brunel University London, Uxbridge, Middlesex, UB8 3PH, United Kingdom.
| | - Yun Chen
- Institute of Information and Control, Hangzhou Dianzi University, Hangzhou 310018, China
| | - Guoliang Wei
- College of Science, University of Shanghai for Science and Technology, Shanghai 200093, China.
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35
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Liu H, Wang Z, Fei W, Li J. H ∞ and l 2-l ∞ state estimation for delayed memristive neural networks on finite horizon: The Round-Robin protocol. Neural Netw 2020; 132:121-130. [PMID: 32871337 DOI: 10.1016/j.neunet.2020.08.006] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Revised: 07/19/2020] [Accepted: 08/10/2020] [Indexed: 11/26/2022]
Abstract
In this paper, a protocol-based finite-horizon H∞ and l2-l∞ estimation approach is put forward to solve the state estimation problem for discrete-time memristive neural networks (MNNs) subject to time-varying delays and energy-bounded disturbances. The Round-Robin protocol is utilized to mitigate unnecessary network congestion occurring in the sensor-to-estimator communication channel. For the delayed MNNs, our aim is to devise an estimator that not only ensures a prescribed disturbance attenuation level over a finite time-horizon, but also keeps the peak value of the estimation error within a given range. By resorting to the Lyapunov-Krasovskii functional method, the delay-dependent criteria are formulated that guarantee the existence of the desired estimator. Subsequently, the estimator gains are obtained via figuring out a bank of convex optimization problems. The validity of our estimator is finally shown via a numerical example.
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Affiliation(s)
- Hongjian Liu
- Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment, Ministry of Education, Anhui Polytechnic University, Wuhu 241000, China; Artificial Intelligence Energy Research Institute, Northeast Petroleum University, Daqing 163318, China.
| | - Zidong Wang
- College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China; Department of Computer Science, Brunel University London, Uxbridge, Middlesex, UB8 3PH, United Kingdom.
| | - Weiyin Fei
- Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment, Ministry of Education, Anhui Polytechnic University, Wuhu 241000, China; School of Mathematics and Physics, Anhui Polytechnic University, Wuhu 241000, China.
| | - Jiahui Li
- Artificial Intelligence Energy Research Institute, Northeast Petroleum University, Daqing 163318, China; Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control, Northeast Petroleum University, Daqing 163318, China.
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36
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Zhu C, Su Z, Xia Y, Li L, Dai J. Event-triggered state estimation for networked systems with correlated noises and packet losses. ISA Trans 2020; 104:36-43. [PMID: 31831149 DOI: 10.1016/j.isatra.2019.11.038] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2018] [Revised: 11/29/2019] [Accepted: 11/29/2019] [Indexed: 06/10/2023]
Abstract
The event-triggered state estimation for the systems suffering from correlated noises and packet losses is considered. A communication mechanism that determines the measurements to be sent or not depending on a specific event-triggered condition is presented to reduce the additional data transmissions. Then a novel event-triggered state estimator related to the trigger threshold and correlation coefficient is proposed. An expected trade-off between the rate of transmission and the estimator performance can be obtained through adjusting the threshold properly, and the influence of noise correlation and packet losses is weakened effectively. The estimator performance is evaluated and certain boundedness conditions for the covariance expectation are obtained. Finally, a target tracking system is supplied to support the relevant results.
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Affiliation(s)
- Cui Zhu
- School of Information and Communication Engineering, Beijing Information Science & Technology University, Beijing 100101, China.
| | - Zhong Su
- Beijing Key Laboratory of High Dynamic Navigation Technology, Beijing Information Science & Technology University, Beijing 100101, China.
| | - Yuanqing Xia
- School of Automation, Beijing Institute of Technology, Beijing 100081, China.
| | - Li Li
- School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China.
| | - Juan Dai
- Beijing Key Laboratory of High Dynamic Navigation Technology, Beijing Information Science & Technology University, Beijing 100101, China.
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37
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Liu J, Yin T, Shen M, Xie X, Cao J. State estimation for cyber-physical systems with limited communication resources, sensor saturation and denial-of-service attacks. ISA Trans 2020; 104:101-114. [PMID: 30654911 DOI: 10.1016/j.isatra.2018.12.032] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/25/2018] [Revised: 11/28/2018] [Accepted: 12/21/2018] [Indexed: 06/09/2023]
Abstract
This paper addresses the issue of the state estimation for cyber-physical systems (CPSs) with limited communication resources, sensor saturation and denial-of-service (DoS) attacks. In order to conveniently handle nonlinear term in CPSs, a Takagi-Sugeno (T-S) fuzzy model is borrowed to approximate it. The event-triggered scheme and quantization mechanism are introduced to relieve the effects brought by limited communication resources. By taking the influence of sensor saturation and DoS attacks into account, a novel mathematical model of state estimation for CPSs is constructed with limited communication resources. By using the Lyapunov stability theory, the sufficient conditions, which can ensure the system exponentially stable, are derived. Moreover, the explicit expressions of the event-based estimator gains are obtained in the form of linear matrix inequalities (LMIs). At last, a simulated example is provided for illustrating the effectiveness of the proposed method.
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Affiliation(s)
- Jinliang Liu
- College of Information Engineering, Nanjing University of Finance and Economics, Nanjing, Jiangsu, PR China; College of Automation Electronic Engineering, Qingdao University of Science and Technology, Qingdao, Shandong, PR China.
| | - Tingting Yin
- College of Information Engineering, Nanjing University of Finance and Economics, Nanjing, Jiangsu, PR China.
| | - Mouquan Shen
- College of Automation and Electrical Engineering, Nanjing University of Technology, Nanjing, Jiangsu, PR China.
| | - Xiangpeng Xie
- Institute of Advanced Technology, Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu, PR China.
| | - Jie Cao
- College of Information Engineering, Nanjing University of Finance and Economics, Nanjing, Jiangsu, PR China.
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38
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Hong Z, Xu L, Chen J. Particle filter combined with data reconciliation for nonlinear state estimation with unknown initial conditions in nonlinear dynamic process systems. ISA Trans 2020; 103:203-214. [PMID: 32471732 DOI: 10.1016/j.isatra.2020.04.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2019] [Revised: 04/09/2020] [Accepted: 04/14/2020] [Indexed: 06/11/2023]
Abstract
State estimation is very crucial for process control and optimization in dynamic processes. The particle filter (PF) is a novel and suitable technique for state estimation of nonlinear dynamic process systems. Conventional PFs for nonlinear dynamic process systems rely on the known initial conditions for state variables, such as the known probability density function (PDF) of initial states or the known values of initial states, but the initial conditions of a nonlinear dynamical system are usually unknown in actual industrial processes. In this paper, a novel methodology, PF combined with data reconciliation, is proposed and applied to nonlinear dynamic process systems for state estimation with unknown initial conditions. The measurement test criterion and data reconciliation with sequentially increasing data information are proposed to derive reliable initial values of the state variables under sufficient information of measurements. The interactive information between PF and data reconciliation problems can improve the initial values iteratively. Finally, accurate results of state estimation can be achieved. The effectiveness of the methodology is demonstrated through two nonlinear dynamic systems.
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Affiliation(s)
- Zhihui Hong
- School of Aerospace Science and Technology, Xidian University, Xi'an Shaanxi, 710126, China
| | - Luping Xu
- School of Aerospace Science and Technology, Xidian University, Xi'an Shaanxi, 710126, China.
| | - Junghui Chen
- Department of Chemical Engineering, Chung-Yuan Christian University, Chung-Li, Taoyuan, Taiwan, 320, R.O.C..
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39
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Yang C, Gao Z, Liu F, Ma R. Extended Kalman filters for nonlinear fractional-order systems perturbed by colored noises. ISA Trans 2020; 102:68-80. [PMID: 31320143 DOI: 10.1016/j.isatra.2019.07.010] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2018] [Revised: 06/28/2019] [Accepted: 07/03/2019] [Indexed: 06/10/2023]
Abstract
The fractional-order extended Kalman filter (FEKF) algorithm for a nonlinear fractional-order system perturbed by the colored noise is presented. Firstly, the first-order Taylor expansion is employed to linearize the nonlinear functions in the estimated system. Then, Grünwald-Letnikov difference (GLD) and the concept of fractional-order average derivative (FOAD) are employed to discretize nonlinear fractional-order systems perturbed by colored fractional-order process or measurement noise. An augmented system determined by the state and colored noises is presented to treat colored noises. Hence, the FEKFs using GLD and FOAD are carried out, respectively. By comparing two kinds of Kalman filters, FEKFs using FODA can gain the better effect of filtering for colored process or measurement noise to raise the estimation precision. Finally, we discuss three examples to show the validity of investigated FEKFs.
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Affiliation(s)
- Chao Yang
- School of Mathematics, Liaoning University, Shenyang 110036, PR China
| | - Zhe Gao
- School of Mathematics, Liaoning University, Shenyang 110036, PR China; College of Light Industry, Liaoning University, Shenyang 110036, PR China.
| | - Fanghui Liu
- School of Mathematics, Liaoning University, Shenyang 110036, PR China
| | - Ruicheng Ma
- School of Mathematics, Liaoning University, Shenyang 110036, PR China
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40
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Yan L, Di C, Wu QMJ, Xia Y, Liu S. Distributed fusion estimation for multisensor systems with non-Gaussian but heavy-tailed noises. ISA Trans 2020; 101:160-169. [PMID: 32111406 DOI: 10.1016/j.isatra.2020.02.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/22/2019] [Revised: 02/02/2020] [Accepted: 02/02/2020] [Indexed: 06/10/2023]
Abstract
Student's t distribution is a useful tool that can model heavy-tailed noises appearing in many practical systems. Although t distribution based filter has been derived, the information filter form is not presented and the data fusion algorithms for dynamic systems disturbed by heavy-tailed noises are rarely concerned. In this paper, based on multivariate t distribution and variational Bayesian estimation, the information filter, the centralized batch fusion, the distributed fusion, and the suboptimal distributed fusion algorithms are derived, respectively. The centralized fusion is given in two forms, namely, from t distribution based filter and the proposed t distribution based information filter, respectively. The distributed fusion is deduced by the use of the newly derived information filter, and it has been demonstrated to be equivalent to the centralized batch fusion. The suboptimal distributed fusion is obtained by a parameter approximation from the derived distributed fusion to decrease the computation complexity. The presented algorithms are shown to be the generalization of the classical Kalman filter based traditional algorithms. Theoretical analysis and exhaustive experimental analysis by a target tracking example show that the proposed algorithms are feasible and effective.
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Affiliation(s)
- Liping Yan
- Key Laboratory of Intelligent Control and Decision of Complex Systems, School of Automation, Beijing Institute of Technology, Beijing 100081, China; Department of Electrical and Computer Engineering, University of Windsor, Windsor N9B3P4, Canada.
| | - Chenying Di
- Key Laboratory of Intelligent Control and Decision of Complex Systems, School of Automation, Beijing Institute of Technology, Beijing 100081, China
| | - Q M Jonathan Wu
- Department of Electrical and Computer Engineering, University of Windsor, Windsor N9B3P4, Canada
| | - Yuanqing Xia
- Key Laboratory of Intelligent Control and Decision of Complex Systems, School of Automation, Beijing Institute of Technology, Beijing 100081, China
| | - Shida Liu
- School of Electrical and Control Engineering, North China University of Technology, Beijing 10093, China
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41
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Ben Messaoud R. Reduced nonlinear unknown inputs observer using MVT and GA. ISA Trans 2020; 101:461-470. [PMID: 32029238 DOI: 10.1016/j.isatra.2020.01.029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/13/2018] [Revised: 01/14/2020] [Accepted: 01/21/2020] [Indexed: 06/10/2023]
Abstract
New construction for nonlinear reduced unknown input observer UIO design is purposed. The main concept consists of using the estimate of error also the parameters used in mean value theorem (MVT) for the observer's design. This observer is founded principally on MVT and the genetic algorithm (GA). A Lyapunov function is utilised for ensuring the stability also the observer's gain is automatically resolved. Lastly, three practical realisations address to the secure communication problem.
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Affiliation(s)
- Ramzi Ben Messaoud
- Laboratoire de nanomatériaux et des systèmes pour les énergies renouvelables, Centre de Recherches et des Technologies de l'Energie, BP. 95, Hammam Lif 2050, Tunisia.
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42
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Guo P, Rivera DE. System Identification Approaches For Energy Intake Estimation: Enhancing Interventions For Managing Gestational Weight Gain. IEEE Trans Control Syst Technol 2020; 28:63-78. [PMID: 31903018 PMCID: PMC6941743 DOI: 10.1109/tcst.2018.2871871] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2023]
Abstract
Excessive maternal weight gain during pregnancy represents a major public health concern that calls for novel and effective gestational weight management interventions. In Healthy Mom Zone (HMZ), an on-going intervention study, energy intake underreporting has been found to be an important consideration that interferes with accurate weight control assessment, and the effective use of energy balance models in an intervention setting. In this paper, a series of estimation approaches that address measurement noise and measurement losses are developed to better understand the extent of energy intake underreporting. These include back-calculating energy intake from an energy balance model developed for gestational weight gain prediction, a Kalman filtering-based approach to recursively estimate energy intake from intermittent measurements in real-time, and an approach based on semi-physical identification principles which features the capability of adjusting future self-reported energy intake by parameterizing the extent of underreporting. The three approaches are illustrated by evaluating with participant data obtained through the HMZ intervention study, with the results demonstrating the potential of these methods to promote the success of weight control. The pros and cons of the presented approaches are discussed to generate insights for users in future applications.
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Affiliation(s)
| | - Daniel E. Rivera
- Control Systems Engineering Laboratory (CSEL), School for Engineering of Matter, Transport, and Energy, Arizona State University, Tempe, AZ, 85281 USA
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43
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Rahmani MR, Farrokhi M. Fractional-order Hammerstein state-space modeling of nonlinear dynamic systems from input-output measurements. ISA Trans 2020; 96:177-184. [PMID: 31285061 DOI: 10.1016/j.isatra.2019.06.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2017] [Revised: 06/04/2019] [Accepted: 06/14/2019] [Indexed: 06/09/2023]
Abstract
This paper introduces a continuous-time fractional-order Hammerstein state-space model with a systematic identification algorithm for modeling nonlinear dynamic systems. The proposed model consists of a radial-basis function neural network followed by a fractional-order system. The proposed identification scheme is accomplished in two stages. The structural parameters of the fractional-order system (i.e. the values of the fractional order and the degree of the denominator in the fractional-order system) are estimated in the frequency domain. Then, the synaptic weights of the radial-basis function neural network and the coefficients of the fractional-order system are determined in the time domain via the Lyapunov stability theory, which guarantees stability of the given method and its convergence under a mild condition. Three examples are provided to show the effectiveness of the proposed method.
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Affiliation(s)
- Mohammad-Reza Rahmani
- School of Electrical Engineering, Iran University of Science and Technology, Tehran 1684613114, Iran.
| | - Mohammad Farrokhi
- School of Electrical Engineering, Center of Excellence for Modeling and Control of Complex Systems, Iran University of Science and Technology, Tehran 1684613114, Iran.
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44
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Abstract
This paper deals with the non-fragile state estimation problem for a class of fractional-order memristive BAM neural networks (FMBAMNNs) with and without time delays for the first time. By means of a novel transformation and interval matrix approach, non-fragile estimators are designed and parameter mismatch problem is averted. Sufficient criteria are established to ascertain the error system is asymptotically stable based on fractional-order Lyapunov functionals and linear matrix inequalities (LMIs). Two examples are put forward to show the effectiveness of the obtained results.
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Affiliation(s)
- Haibo Bao
- School of Mathematics and Statistics, Southwest University, Chongqing 400715, China.
| | - Ju H Park
- Nonlinear Dynamics Group, Department of Electrical Engineering, Yeungnam University, 280 Daehak-Ro, Kyongsan 38541, Republic of Korea.
| | - Jinde Cao
- School of Mathematics, Southeast University, Nanjing 210096, China.
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45
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Noor-A-Rahim M, Khyam MO, Li X, Pesch D. Sensor Fusion and State Estimation of IoT Enabled Wind Energy Conversion System. Sensors (Basel) 2019; 19:E1566. [PMID: 30939747 DOI: 10.3390/s19071566] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/31/2019] [Revised: 03/21/2019] [Accepted: 03/26/2019] [Indexed: 12/02/2022]
Abstract
The use of renewable energy has increased dramatically over the past couple of decades. Wind farms, consisting of wind turbines, play a vital role in the generation of renewable energy. For monitoring and maintenance purposes, a wind turbine has a variety of sensors to measure the state of the turbine. Sensor measurements are transmitted to a control center, which is located away from the wind farm, for monitoring and maintenance purposes. It is therefore desirable to ensure reliable wireless communication between the wind turbines and the control center while integrating the observations from different sensors. In this paper, we propose an IoT based communication framework for the purpose of reliable communication between wind turbines and control center. The communication framework is based on repeat-accumulate coded communication to enhance reliability. A fusion algorithm is proposed to exploit the observations from multiple sensors while taking into consideration the unpredictable nature of the wireless channel. The numerical results show that the proposed scheme can closely predict the state of a wind turbine. We also show that the proposed scheme significantly outperforms traditional estimation schemes.
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Rüschen D, Rimke M, Gesenhues J, Leonhardt S, Walter M. Online cardiac output estimation during transvalvular left ventricular assistance. Comput Methods Programs Biomed 2019; 171:87-97. [PMID: 27609634 DOI: 10.1016/j.cmpb.2016.08.020] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2015] [Revised: 08/09/2016] [Accepted: 08/25/2016] [Indexed: 06/06/2023]
Abstract
BACKGROUND AND OBJECTIVES Sufficient cardiac output is one of the main goals of ventricular assist device therapy. To date, there is no adequate method to estimate the combined amount of blood the native heart and a continuous-flow assist device pump through the circulatory system. This paper presents an approach to estimate total cardiac output based on the signals provided by optical pressure sensors mounted on the inlet and outlet of an Abiomed Impella CP pump. METHODS Two Kalman filters were used in parallel for joint estimation of the aortic flow rate and the hydraulic resistance of the aortic valve. The filters utilized a third order nonlinear state-space representation of the cardiovascular system with two nominal parameter sets, one for ovine and another for human subjects. The accuracy of the estimated cardiac output has been investigated in a hybrid mock circulatory loop and an animal study involving two sheep with experimentally induced acute ischaemic heart disease supported by a transvalvular left ventricular assist device. RESULTS The in vitro accuracy of the cardiac output estimation is ±3.64%. In an ovine model, the comparison of the estimated cardiac output with an ultrasonic flow measurement in the pulmonary artery showed 95% limits of agreement of -0.004 ± 0.897 L min-1. The estimation errors were comparable to the accuracy of the measurement (±10%), which is the gold standard in research for invasive blood flow diagnostics. CONCLUSIONS The online estimation of total cardiac output may give the treating physician a direct and physiologically meaningful feedback on the pump speed setting. One promising possible application of our method is physiological control, where the cardiac output can be used as the control variable for closed-loop ventricular assist device therapy.
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Affiliation(s)
- Daniel Rüschen
- Philips Chair for Medical Information Technology, Helmholtz-Institute for Biomedical Engineering, RWTH Aachen University, Aachen, Germany.
| | - Miriam Rimke
- Philips Chair for Medical Information Technology, Helmholtz-Institute for Biomedical Engineering, RWTH Aachen University, Aachen, Germany
| | - Jonas Gesenhues
- Institute of Automatic Control, RWTH Aachen University, Aachen, Germany
| | - Steffen Leonhardt
- Philips Chair for Medical Information Technology, Helmholtz-Institute for Biomedical Engineering, RWTH Aachen University, Aachen, Germany
| | - Marian Walter
- Philips Chair for Medical Information Technology, Helmholtz-Institute for Biomedical Engineering, RWTH Aachen University, Aachen, Germany
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Messaoud RB. Observer for nonlinear systems using mean value theorem and particle swarm optimization algorithm. ISA Trans 2019; 85:226-236. [PMID: 30401488 DOI: 10.1016/j.isatra.2018.10.036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2018] [Revised: 09/18/2018] [Accepted: 10/19/2018] [Indexed: 06/08/2023]
Abstract
A new observer design for nonlinear systems is considered. The main idea consist of the determination of the estimation error and mean value theorem parameters (β) to introduce them into proposed observer structure. This process is designed on basis of mean value theorem (MVT) and Particle Swarm Optimization algorithm (PSO). This observer does not use Linear Matrix Inequality technique (LMI) for stability study. The stability study relies on the use of a classical quadratic Lyapunov function. The observer's gains are determined systematically. For the validation of theoretical development proposed in this paper. We consider two practical realization that deals the secure communication problem and a statistical performance analysis is realized.
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Affiliation(s)
- Ramzi Ben Messaoud
- Laboratoire de nanomatériaux et des systèmes pour les énergies renouvelables, Centre de Recherches et des Technologies de l'Energie Technopole Borij, Cedria, Hammam Lif, Tunisia.
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Lamien B, Rangel Barreto Orlande H, Antonio Bermeo Varón L, Leite Queiroga Basto R, Enrique Eliçabe G, Silva Dos Santos D, Machado Cotta R. Estimation of the temperature field in laser-induced hyperthermia experiments with a phantom. Int J Hyperthermia 2018; 35:279-290. [PMID: 30204008 DOI: 10.1080/02656736.2018.1496283] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023] Open
Abstract
BACKGROUND One of the challenges faced during the hyperthermia treatment of cancer is to monitor the temperature distribution in the region of interest. The main objective of this work was to accurately estimate the transient temperature distribution in the heated region, by using a stochastic heat transfer model and temperature measurements. METHODS Experiments involved the laser heating of a cylindrical phantom, partially loaded with iron oxide nanoparticles. The nanoparticles were manufactured and characterized in this work. The solution of the state estimation problem was obtained with an algorithm of the Particle Filter method, which allowed for simultaneous estimation of state variables and model parameters. Measurements of one single sensor were used for the estimation procedure, which is highly desirable for practical applications in order to avoid patient discomfort. RESULTS Despite the large uncertainties assumed for the model parameters and for the coupled radiation-conduction model, discrepancies between estimated temperatures and internal measurements were smaller than 0.7 °C. In addition, the estimated fluence rate distribution was physically meaningful. Maximum discrepancies between the prior means and the estimated means were of 2% for thermal conductivity and heat transfer coefficient, 4% for the volumetric heat capacity and 3% for the irradiance. CONCLUSIONS This article demonstrated that the Particle Filter method can be used to accurately predict the temperatures in regions where measurements are not available. The present technique has potential applications in hyperthermia treatments as an observer for active control strategies, as well as to plan personalized heating protocols.
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Affiliation(s)
- Bernard Lamien
- a Department of Mechanical Engineering , Politécnica/COPPE Federal University of Rio de Janeiro, Rio de Janeiro , Brazil
| | - Helcio Rangel Barreto Orlande
- a Department of Mechanical Engineering , Politécnica/COPPE Federal University of Rio de Janeiro, Rio de Janeiro , Brazil.,b Department of Nanotechnology Engineering , COPPE Federal University of Rio de Janeiro, Rio de Janeiro , Brazil
| | - Leonardo Antonio Bermeo Varón
- a Department of Mechanical Engineering , Politécnica/COPPE Federal University of Rio de Janeiro, Rio de Janeiro , Brazil.,c Department of Bioengineering , University of Santiago de Cali, Santiago de Cali , Colombia
| | - Rodrigo Leite Queiroga Basto
- a Department of Mechanical Engineering , Politécnica/COPPE Federal University of Rio de Janeiro, Rio de Janeiro , Brazil
| | - Guillermo Enrique Eliçabe
- d Institute of Materials Science and Technology (INTEMA), University of Mar del Plata, Mar del Plata , Argentina.,e National Research Council (CONICET ), Buenos Aires, Argentina
| | - Dilson Silva Dos Santos
- b Department of Nanotechnology Engineering , COPPE Federal University of Rio de Janeiro, Rio de Janeiro , Brazil.,f Department of Metallurgical and Materials Engineering , Politécnica/COPPE Federal University of Rio de Janeiro, Rio de Janeiro , Brazil
| | - Renato Machado Cotta
- a Department of Mechanical Engineering , Politécnica/COPPE Federal University of Rio de Janeiro, Rio de Janeiro , Brazil
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Das L, Kumar G, Rengaswamy R, Srinivasan B. A novel approach for benchmarking and assessing the performance of state estimators. ISA Trans 2018; 80:137-145. [PMID: 29958650 DOI: 10.1016/j.isatra.2018.06.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2018] [Revised: 05/27/2018] [Accepted: 06/11/2018] [Indexed: 06/08/2023]
Abstract
State estimation is a widely adopted soft sensing technique that incorporates predictions from an accurate model of the process and measurements to provide reliable estimates of unmeasured variables. The reliability of such estimators is threatened by measurement related challenges and model inaccuracies. In this article, a method for benchmarking of state estimation techniques is proposed. This method can be used to quantify the performance and hence reliability of an estimator. The Hurst exponents of a posteriori filtering errors are analyzed to characterize a benchmark (minimum mean squared error) estimator, similar to the minimum variance control benchmark developed for control loops. A distance metric is then used to quantify the extent of deviation of an estimator from the benchmark. The proposed technique is developed for linear systems and extended to non-linear systems with single as well as multiple measurable variables. Simulation studies are carried out with Kalman based as well as Monte Carlo based estimators whose computational details are significantly different. Results reveal that the technique serves as a tool that can quantify the performance and assess the reliability of a state estimator. The strengths and limitations of the proposed technique are discussed with guidelines on applications and deployment of the technique in a real life system.
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Affiliation(s)
- Laya Das
- Department of Electrical Engineering, Indian Institute of Technology Gandhinagar, Gandhinagar, 382355, Gujarat, India
| | - Gaurav Kumar
- Department of Electrical Engineering, Indian Institute of Technology Gandhinagar, Gandhinagar, 382355, Gujarat, India
| | - Raghunathan Rengaswamy
- Department of Chemical Engineering, Indian Institute of Technology Madras, Chennai, 600036, Tamil Nadu, India
| | - Babji Srinivasan
- Department of Chemical Engineering, Indian Institute of Technology Gandhinagar, Gandhinagar, 382355, Gujarat, India.
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Zhao S, Shmaliy YS, Liu F. Fast Kalman-like optimal FIR filter for time-variant systems with improved robustness. ISA Trans 2018; 80:160-168. [PMID: 30054034 DOI: 10.1016/j.isatra.2018.07.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/25/2016] [Revised: 05/02/2018] [Accepted: 07/13/2018] [Indexed: 06/08/2023]
Abstract
In this paper, a fast Kalman-like iterative OFIR algorithm is proposed for discrete-time filtering of linear time-varying dynamic systems. The batch OFIR filter is re-derived in an alternative way to show that this filter is unique for such systems. A computationally efficient fast iterative form is found for the OFIR filter using recursions. It is shown that each recursion has the Kalman filter (KF) predictor/corrector format with initial conditions specified via measurements on a horizon of N nearest past points. In this regard, the KF is considered as a special case of the iterative OFIR filtering algorithm when N goes to infinity. Applications are given for the 3-state target tracking and three-degree-of-freedom (DOF) hover system. It has been shown experimentally that the proposed iterative OFIR algorithm operates much faster than the batch OFIR filter and has the computational complexity acceptable for real-time applications. It has also been demonstrated by simulations that an increase in the number of the states results in better robustness of the OFIR filter against temporary model uncertainties and in higher immunity against errors in the noise statistics.
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Affiliation(s)
- Shunyi Zhao
- Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Institute of Automation, Jiangnan University, Wuxi 214122, PR China.
| | - Yuriy S Shmaliy
- Department of Electronics Engineering, Universidad de Guanajuato, Salamanca 36885, Mexico.
| | - Fei Liu
- Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Institute of Automation, Jiangnan University, Wuxi 214122, PR China.
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