1
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Fang Q, Mao P. Compliant Contact Force Control for Aerial Manipulator of Adaptive Neural Network-Based Robust Control. Sensors (Basel) 2024; 24:2556. [PMID: 38676173 PMCID: PMC11053767 DOI: 10.3390/s24082556] [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] [Received: 02/05/2024] [Revised: 04/10/2024] [Accepted: 04/13/2024] [Indexed: 04/28/2024]
Abstract
Aerial manipulators expand the application scenarios of manipulators into the air. To complete various operations, the contact force between the aerial manipulator and the target must be precisely controlled. In this study, we first established the mathematical models of the multirotor and the manipulator separately. Their mutual influence is regarded as each other's disturbance, and the overall linkage mechanism is established through analysis. Then, a robust sliding mode control strategy is developed for accurate trajectory tracking. The controller is derived from Lyapunov theory, which can ensure the stability of the closed-loop system. To compensate for the effect of system uncertainty, an adaptive radial basis function neural network is devised to approximate the part of the controller containing the model information. In addition, an impedance controller is designed to convert force control into position control to make the manipulator contact with the target compliantly. Finally, the simulation and experimental results indicate that the proposed method can guarantee the accuracy of the contact force and has good robustness.
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Affiliation(s)
| | - Pengjun Mao
- School of Mechanical and Electrical Engineering, Henan University of Science and Technology, Luoyang 471003, China;
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2
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Yang Q, Ouyang K, Yang L, Fu R, Hu P. A Novel Combined Method for Measuring the Three-Dimensional Rotational Angle of a Spherical Joint. Sensors (Basel) 2023; 24:90. [PMID: 38202950 PMCID: PMC10781326 DOI: 10.3390/s24010090] [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] [Received: 11/07/2023] [Revised: 12/20/2023] [Accepted: 12/21/2023] [Indexed: 01/12/2024]
Abstract
To improve the measurement accuracy of the three-dimensional rotation angle of a spherical joint, a novel approach is proposed in this study, which combines magnetic detection by a Hall sensor and surface feature identification by an eddy current sensor. Firstly, a permanent magnet is embedded in the ball head of a spherical joint, and Hall sensors are set and distributed in the ball socket to measure the variation in the magnetic flux density when the spherical joint rotates, which are related to the 3D rotation angle. In order to further improve the measurement accuracy and robustness, we also set grooves on the ball head and use eddy current sensors to synchronously identify the rotation angle of the ball head. After the combination of two signals is performed, a measurement model is established using the RBF neural network by training, and the real-time measurement of the 3D rotation angle of the spherical joint is realized. The feasibility and superiority of this method are validated through experiments. The experimental results indicate that the measurement accuracy is substantially promoted compared to the preliminary measurement scheme based on spherical coding; the average measurement error of the single axis is reduced by 9'9″. The root mean square errors for the measurements of the 3D rotation angles in this proposed method are as follows: pitch angle α has an error of 1'8″, yaw angle β has an error of 2'15″, and roll angle γ has an error of 29'6″.
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Affiliation(s)
| | | | | | | | - Penghao Hu
- Anhui Province Key Laboratory of Measuring Theory and Precision Instrument, School of Instrument Science and Opto-Electronic Engineering, Hefei University of Technology, Hefei 230009, China; (Q.Y.); (K.O.); (L.Y.); (R.F.)
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3
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Guo L. Analysis and prediction of athlete's anxiety state based on artificial intelligence. PeerJ Comput Sci 2023; 9:e1322. [PMID: 37346592 PMCID: PMC10280679 DOI: 10.7717/peerj-cs.1322] [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] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 03/09/2023] [Indexed: 06/23/2023]
Abstract
Obtaining athletes' anxiety accurately and regulating their psychological state helps improve their competitive performance. Therefore, this article uses a hierarchical clustering algorithm to identify the sources of stress of track and field athletes. A novel and efficient hierarchical clustering algorithm is proposed in this article. The algorithm consists of two stages: dividing and agglomerating. In the dividing stage, the initial data set is taken as a class and subclasses more than the actual number of clusters are obtained through multiple dividing. In the agglomerating phase, the subclasses divided during the dividing process are merged into the correct class. In addition, we construct an analysis model of athletes' anxiety state based on the radial basis function (RBF) model, where athletes' anxiety is divided into three categories: physical condition anxiety, competition state and cognitive state. The proposed model is trained from the official website of the China Track and Field Association. The athletes' information from 500 samples was arranged to form the sample database of athletes' data. The implicit unit center, function width and connection weight record the characteristics of various sports anxiety states. Then we used the Bayesian and Lagrange models as comparative models for evaluating the psychological state. Precision and efficiency were used for evaluation indexes. The proposed model's results are much better in accuracy and time than those of the Lagrange and Bayesian models. The outcome of the proposed research can provide a reasonable basis for the decision-making of stress relief for track and field athletes.
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4
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Zhang J, Wu Y, Li Q. Production Change Optimization Model of Nonlinear Supply Chain System under Emergencies. Sensors (Basel) 2023; 23:3718. [PMID: 37050778 PMCID: PMC10098574 DOI: 10.3390/s23073718] [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] [Figures] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 03/26/2023] [Accepted: 03/27/2023] [Indexed: 06/19/2023]
Abstract
Aiming at the problem that the upstream manufacturer cannot accurately formulate the production plan after the link of the nonlinear supply chain system changes under emergencies, an optimization model of production change in a nonlinear supply chain system under emergencies is designed. Firstly, based on the structural characteristics of the supply chain system and the logical relationship between production, sales, and storage parameters, a three-level single-chain nonlinear supply chain dynamic system model containing producers, sellers, and retailers was established based on the introduction of nonlinear parameters. Secondly, the radial basis function (RBF) neural network and improved fast variable power convergence law were introduced to improve the traditional sliding mode control, and the improved adaptive sliding mode control is proposed so that it can have a good control effect on the unknown nonlinear supply chain system. Finally, based on the numerical assumptions, the constructed optimization model was parameterized and simulated for comparison experiments. The simulation results show that the optimized model can reduce the adjustment time by 37.50% and inventory fluctuation by 42.97%, respectively, compared with the traditional sliding mode control, while helping the supply chain system to return the smooth operation after the change within 5 days.
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Affiliation(s)
- Jing Zhang
- School of Automation, Beijing Information Science and Technology University (BISTU), Beijing 100192, China
- Institute of Intelligent Networked Things and Cooperative Control, Beijing Information Science and Technology University (BISTU), Beijing 100192, China
| | - Yingnian Wu
- School of Automation, Beijing Information Science and Technology University (BISTU), Beijing 100192, China
- Institute of Intelligent Networked Things and Cooperative Control, Beijing Information Science and Technology University (BISTU), Beijing 100192, China
- Intelligent Perception and Control of High-End Equipment Beijing International Science and Technology Cooperation Base, Beijing Information Science and Technology University (BISTU), Beijing 100192, China
| | - Qingkui Li
- School of Automation, Beijing Information Science and Technology University (BISTU), Beijing 100192, China
- Institute of Intelligent Networked Things and Cooperative Control, Beijing Information Science and Technology University (BISTU), Beijing 100192, China
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5
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Zhang N, Yang S, Wu G, Ding H, Zhang Z, Guo K. Fast Distributed Model Predictive Control Method for Active Suspension Systems. Sensors (Basel) 2023; 23:3357. [PMID: 36992069 PMCID: PMC10055814 DOI: 10.3390/s23063357] [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] [Figures] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 03/12/2023] [Accepted: 03/16/2023] [Indexed: 06/19/2023]
Abstract
In order to balance the performance index and computational efficiency of the active suspension control system, this paper offers a fast distributed model predictive control (DMPC) method based on multi-agents for the active suspension system. Firstly, a seven-degrees-of-freedom model of the vehicle is created. This study establishes a reduced-dimension vehicle model based on graph theory in accordance with its network topology and mutual coupling constraints. Then, for engineering applications, a multi-agent-based distributed model predictive control method of an active suspension system is presented. The partial differential equation of rolling optimization is solved by a radical basis function (RBF) neural network. It improves the computational efficiency of the algorithm on the premise of satisfying multi-objective optimization. Finally, the joint simulation of CarSim and Matlab/Simulink shows that the control system can greatly minimize the vertical acceleration, pitch acceleration, and roll acceleration of the vehicle body. In particular, under the steering condition, it can take into account the safety, comfort, and handling stability of the vehicle at the same time.
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Affiliation(s)
- Niaona Zhang
- School of Electrical and Electronic Engineering, Changchun University of Technology, Changchun 130012, China
- State Key Laboratory of Automobile Simulation and Control, Jilin University, Changchun 130012, China
| | - Sheng Yang
- School of Electrical and Electronic Engineering, Changchun University of Technology, Changchun 130012, China
| | - Guangyi Wu
- School of Electrical and Electronic Engineering, Changchun University of Technology, Changchun 130012, China
| | - Haitao Ding
- State Key Laboratory of Automobile Simulation and Control, Jilin University, Changchun 130012, China
| | - Zhe Zhang
- State Key Laboratory of Automobile Simulation and Control, Jilin University, Changchun 130012, China
| | - Konghui Guo
- State Key Laboratory of Automobile Simulation and Control, Jilin University, Changchun 130012, China
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6
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Bowen W. Research on nonlinear calibration of mine catalytic-combustion-based combustible-gas sensor based on RBF neural network. Heliyon 2023; 9:e14055. [PMID: 36915543 PMCID: PMC10006743 DOI: 10.1016/j.heliyon.2023.e14055] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 02/18/2023] [Accepted: 02/20/2023] [Indexed: 03/02/2023] Open
Abstract
After using a catalytic-combustion-based combustible-gas sensor (catalytic sensor) underground for a period of time, the sensitivity drifts due to environmental factors such as coal dust, temperature, and humidity. It is necessary to adjust the sensor regularly to ensure its accuracy. In this paper, RBF neural network technology is introduced to fit a nonlinear continuous function to solve the problem of the output error of the sensor being too large due to linear adjustment. Through experimental analysis, it is demonstrated that the RBF neural network model has a higher convergence speed and smaller error than other network models. By embedding the RBF network model into a sensor microcontroller, the error of traditional linear calibration can be reduced by two orders of magnitude and the measurement accuracy of the catalytic sensor can be greatly improved.
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Affiliation(s)
- Wang Bowen
- China Coal Technology Engineering Group, Chongqing Research Institute, Chongqing, 410000, China
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7
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Liu H, Tu H, Huang S, Zheng X. Adaptive Predefined-Time Sliding Mode Control for QUADROTOR Formation with Obstacle and Inter-Quadrotor Avoidance. Sensors (Basel) 2023; 23:2392. [PMID: 36904594 PMCID: PMC10007529 DOI: 10.3390/s23052392] [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] [Figures] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 02/15/2023] [Accepted: 02/19/2023] [Indexed: 06/18/2023]
Abstract
In this paper, aiming at the problem of control and obstacle avoidance in quadrotor formation when mathematical modeling is not accurate, the artificial potential field method with virtual force is used to plan the obstacle avoidance path of quadrotor formation to solve the problem that the artificial potential field method may fall into local optimal. The adaptive predefined-time sliding mode control algorithm based on RBF neural networks enables the quadrotor formation to track the planned trajectory in a predetermined time and also adaptively estimates the unknown interference in the mathematical model of the quadrotor to improve the control performance. Through theoretical derivation and simulation experiments, this study verified that the proposed algorithm can make the planned trajectory of the quadrotor formation avoid obstacles and make the error between the true trajectory and the planned trajectory converge within a predetermined time under the premise of adaptive estimation of unknown interference in the quadrotor model.
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8
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Sun X, Zhou Z, Wang Y. Water resource carrying capacity and obstacle factors in the Yellow River basin based on the RBF neural network model. Environ Sci Pollut Res Int 2023; 30:22743-22759. [PMID: 36306066 PMCID: PMC9613451 DOI: 10.1007/s11356-022-23712-3] [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] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 10/14/2022] [Indexed: 06/16/2023]
Abstract
The Yellow River basin (YRB) plays an important role in China's economic and social growth. Based on different dimensions, we adopted the radial basis function (RBF) neural network model and the obstacle degree model to examine the water resource carrying capacity (WRCC) of the YRB. From 2005 to 2020, the WRCC of the entire YRB, as well as the upstream and midstream regions, improved, but the WRCC of the downstream region remained poor, revealing spatial differences. The overall improvement in the WRCC of the Yellow River's nine provinces is good, but the WRCC of Inner Mongolia and Henan is poor, suggesting regional differences. From the standpoint of obstacle factors, the development and usage rate of surface water resources are the main challenges. In 2020, the obstacle degree of the YRB reached 87.4871%. The irrigated area rate in Gansu was the primary obstacle factor, and the obstacle degree reached 73.0238%. Qinghai's industrial aspects mostly hindered the improvement of its WRCC, with an obstacle degree of 31.36%. The results provide a theoretical reference for the high-quality development of the YRB.
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Affiliation(s)
- Xinrui Sun
- School of Statistics, Dongbei University of Finance and Economics, Dalian, 116023, China
| | - Zixuan Zhou
- School of Statistics, Dongbei University of Finance and Economics, Dalian, 116023, China
| | - Yong Wang
- School of Statistics, Dongbei University of Finance and Economics, Dalian, 116023, China.
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9
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Lu Y, Zheng N, Ye M, Zhu Y, Zhang G, Nazemi E, He J. Proposing Intelligent Approach to Predicting Air Kerma within Radiation Beams of Medical X-ray Imaging Systems. Diagnostics (Basel) 2023; 13:diagnostics13020190. [PMID: 36673000 PMCID: PMC9858575 DOI: 10.3390/diagnostics13020190] [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] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 12/17/2022] [Accepted: 12/30/2022] [Indexed: 01/06/2023] Open
Abstract
The air kerma is a key parameter in medical diagnostic radiology. Radiologists use the air kerma parameter to evaluate organ doses and any associated patient hazards. The air kerma can be simply described as the deposited kinetic energy once a photon passes through the air, and it represents the intensity of the radiation beam. Due to the heel effect in the X-ray sources of medical imaging systems, the air kerma is not uniform within the X-ray beam's field of view. Additionally, the X-ray tube voltage can also affect this nonuniformity. In this investigation, an intelligent technique based on the radial basis function neural network (RBFNN) is presented to predict the air kerma at every point within the fields of view of the X-ray beams of medical diagnostic imaging systems based on discrete and limited measured data. First, a diagnostic imaging system was modeled with the help of the Monte Carlo N Particle X version (MCNPX) code. It should be noted that a tungsten target and beryllium window with a thickness of 1 mm (no extra filter was applied) were used for modeling the X-ray tube. Second, the air kerma was calculated at various discrete positions within the conical X-ray beam for tube voltages of 40 kV, 60 kV, 80 kV, 100 kV, 120 kV, and 140 kV (this range covers most medical X-ray imaging applications) to provide the adequate dataset for training the network. The X-ray tube voltage and location of each point at which the air kerma was calculated were used as the RBFNN inputs. The calculated air kerma was also assigned as the output. The trained RBFNN model was capable of estimating the air kerma at any random position within the X-ray beam's field of view for X-ray tube voltages within the range of medical diagnostic radiology (20-140 kV).
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Affiliation(s)
- Yanjie Lu
- Department of Digital Media Technology, Hangzhou Dianzi University, Hangzhou 310018, China
| | - Nan Zheng
- College of Pharmacy, Wenzhou Medical University, Wenzhou 325035, China
| | - Mingtao Ye
- Department of Digital Media Technology, Hangzhou Dianzi University, Hangzhou 310018, China
| | - Yihao Zhu
- School of Economics and Management, Zhejiang Ocean University, Zhoushan 316022, China
| | - Guodao Zhang
- Department of Digital Media Technology, Hangzhou Dianzi University, Hangzhou 310018, China
- Correspondence: (G.Z.); (E.N.); (J.H.)
| | - Ehsan Nazemi
- Department of Physics, University of Antwerp, 2610 Antwerp, Belgium
- Correspondence: (G.Z.); (E.N.); (J.H.)
| | - Jie He
- The First People’s Hospital of Fuyang District, Hangzhou 310000, China
- Correspondence: (G.Z.); (E.N.); (J.H.)
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10
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Wang Y, Zhang T, Li J, Ren J. Neural network-based adaptive event-triggered sliding mode control for singular systems with an adaptive event-triggering communication scheme. ISA Trans 2022; 129:15-27. [PMID: 35232572 DOI: 10.1016/j.isatra.2022.02.020] [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: 02/15/2021] [Revised: 02/09/2022] [Accepted: 02/09/2022] [Indexed: 06/14/2023]
Abstract
This paper studies the event-triggered sliding mode control problem for singular systems subject to the unknown nonlinear function and the exogenous disturbance. For saving the communication resources, a new adaptive event-triggering communication scheme (AETCS) is designed, which scheme uses the information on the nonlinear function part. Secondly, for the error system, we provide a novel integral sliding surface, which makes it beneficial to construct a new augmented delay system model by utilizing a delay system method. Furthermore, the sliding mode control (SMC) method for the error system is applied to compensate the unknown nonlinearity by using its estimate and match the exogenous disturbance by its upper bound. According to the Lyapunov function theory, stability criteria are got on the basis of LMIs. Moreover, two novel event-triggered adaptive sliding mode controllers based on RBF neural network are designed such that reachability conditions are obtained, and the asymptotic stability of singular systems with the H∞ performance is guaranteed. The RBF neural networks way is exploited to evaluate the unknown nonlinear function, which can eliminate the strict assumption of nonlinear function in some existing results. Finally, the proposed method is validated by two examples.
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Affiliation(s)
- Yuzhong Wang
- Department of Mathematics, Northeastern University, Shenyang, Liaoning, 110819, PR China.
| | - Tie Zhang
- Department of Mathematics, Northeastern University, Shenyang, Liaoning, 110819, PR China; Department of Mathematics and the State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110004, PR China.
| | - Jinna Li
- School of Information and Control Engineering, Liaoning Shihua University, Fushun 113001, PR China; Department of Sciences, Shenyang University of Chemical Technology, Shenyan 110142, PR China
| | - Junchao Ren
- Department of Mathematics, Northeastern University, Shenyang, Liaoning, 110819, PR China
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11
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Feng H, Song Q, Ma S, Ma W, Yin C, Cao D, Yu H. A new adaptive sliding mode controller based on the RBF neural network for an electro-hydraulic servo system. ISA Trans 2022; 129:472-484. [PMID: 35067353 DOI: 10.1016/j.isatra.2021.12.044] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2021] [Revised: 10/25/2021] [Accepted: 12/15/2021] [Indexed: 05/13/2023]
Abstract
Accuracy and robust trajectory tracking for electro-hydraulic servo systems in the presence of load disturbances and model uncertainties are of great importance in many fields. In this work, a new adaptive sliding mode control method based on the RBF neural networks (SMC-RBF) is proposed to improve the performances of a robotic excavator. Model uncertainties and load disturbances of the electro-hydraulic servo system are approximated and compensated using the RBF neural networks. Adaptive mechanisms are designed to adjust the connection weights of the RBF neural networks in real time to guarantee the stability. A nonlinear term is introduced into the sliding mode to design an adaptive terminal sliding mode control structure to improve dynamic performances and the convergence speed. Moreover, a sliding mode chattering reduction method is proposed to suppress the chattering phenomenon. Three types of step, ramp and sine signals are used as the simulation reference trajectories to compare different controllers on a co-simulation platform. Experiments with leveling and triangle conditions are presented on a robotic excavator. Results show that the proposed SMC-RBF controller is superior to existing proportional integral derivative (PID) and sliding mode controller (SMC) in terms of tracking accuracy and disturbance rejection.
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Affiliation(s)
- Hao Feng
- School of Artificial Intelligence, Nanjing University of Information Science & Technology, Nanjing 210044, China.
| | - Qianyu Song
- School of Artificial Intelligence, Nanjing University of Information Science & Technology, Nanjing 210044, China
| | - Shoulei Ma
- United Institute of Excavator Key Technology, Nanjing Tech University, Nanjing 211816, China
| | - Wei Ma
- United Institute of Excavator Key Technology, Nanjing Tech University, Nanjing 211816, China
| | - Chenbo Yin
- United Institute of Excavator Key Technology, Nanjing Tech University, Nanjing 211816, China
| | | | - Hongfu Yu
- SANY Group Co., Ltd., Suzhou 215300, China
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12
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Wang Z, Zhou J, Ren J, Shu A. Predicting Surface Residual Stress for Multi-Axis Milling of Ti-6Al-4V Titanium Alloy in Combined Simulation and Experiments. Materials (Basel) 2022; 15:6471. [PMID: 36143785 PMCID: PMC9501299 DOI: 10.3390/ma15186471] [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] [Figures] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 09/09/2022] [Accepted: 09/13/2022] [Indexed: 06/16/2023]
Abstract
As one essential indicator of surface integrity, residual stress has an important influence on the fatigue performance of aero engines' thin-walled parts. Larger compressive or smaller tensile residual stress is more prone to causing fatigue cracks. To optimize the state of residual stress, the relationship between the surface residual stress and the machining conditions is studied in this work. A radial basis function (RBF) neural network model based on simulated and experimental data is developed to predict the surface residual stress for multi-axis milling of Ti-6Al-4V titanium alloy. Firstly, a 3D numerical model is established and verified through a cutting experiment. These results are found to be in good agreement with average absolute errors of 11.6% and 15.2% in the σx and σy directions, respectively. Then, the RBF neural network is introduced to relate the machining parameters with the surface residual stress using simulated and experimental samples. A good correlation is observed between the experimental and the predicted results. The verification shows that the average prediction error rate is 14.4% in the σx direction and 17.2% in the σy direction. The effects of the inclination angle, cutting speed, and feed rate on the surface residual stress are investigated. The results show that the influence of machining parameters on surface residual stress is nonlinear. The proposed model provides guidance for the control of residual stress in the precision machining of complex thin-walled structures.
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13
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Zhang Z, Chen Y, Wu Y, Lin L, He B, Miao Z, Wang Y. Gliding grasping analysis and hybrid force/position control for unmanned aerial manipulator system. ISA Trans 2022; 126:377-387. [PMID: 34446280 DOI: 10.1016/j.isatra.2021.07.038] [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: 02/23/2021] [Revised: 06/25/2021] [Accepted: 07/28/2021] [Indexed: 06/13/2023]
Abstract
In this paper, considering the control difficulty of the unmanned aerial manipulator (UAM) interacting with environments, a force analysis during gliding grasping and a hybrid force/position control strategy are proposed for the UAM to enhance control performances during dynamic gliding grasping respectively. First, the instantaneous contact force during the gliding grasping is analyzed by the impulse and momentum theorem, and some factors affecting grasping performance are considered to complete an analysis of grasping force including the irregular shape of the object, the object scrolling, and geometrically asymmetric grasping. Meanwhile, the mass of the grasping object and the inertia tensor are considered unknown bounded items. As a benefit, an accurate dynamics model of the UAM gliding grasping is guaranteed. Second, a hybrid force/position controller based on an adaptive neural network estimator is adopted for UAM to overcome both internal disturbances and external disturbances. The proposed method stability is analyzed through the Lyapunov stability theory. Finally, through a dynamic gliding grasping simulation, the effectiveness and superiority of the proposed scheme are verified.
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Affiliation(s)
- Zhenguo Zhang
- School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, 350108, China.
| | - Yanjie Chen
- School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, 350108, China; National Engineering Laboratory for Robot Visual Perception and Control Technology, Changsha, 410082, China.
| | - Yangning Wu
- School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, 350108, China.
| | - Lixiong Lin
- School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, 350108, China.
| | - Bingwei He
- School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, 350108, China.
| | - Zhiqiang Miao
- College of Electrical and Information Engineering, Hunan University, Changsha, 410082, China; National Engineering Laboratory for Robot Visual Perception and Control Technology, Changsha, 410082, China.
| | - Yaonan Wang
- College of Electrical and Information Engineering, Hunan University, Changsha, 410082, China; National Engineering Laboratory for Robot Visual Perception and Control Technology, Changsha, 410082, China.
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14
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Shen W, Wang J. An integral terminal sliding mode control scheme for speed control system using a double-variable hydraulic transformer. ISA Trans 2022; 124:386-394. [PMID: 31648794 DOI: 10.1016/j.isatra.2019.08.068] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2019] [Revised: 08/19/2019] [Accepted: 08/31/2019] [Indexed: 06/10/2023]
Abstract
In recent years, with the aggravation of energy crisis and environmental pollution, the Common Pressure Rail (CPR) system with great energy saving potential has become a research hotspot in the hydraulic research area. However, as the key component of CPR system, hydraulic transformers have the problems of low efficiency and poor control effect, which limit its practical application. The main contribution of the paper is to propose a new type of double-variable hydraulic transformer (DVHT) and address the control issue. As the displacement is adjustable, DVHT can achieve pressure regulation while maintaining the cylinder speed in the high-efficiency area. According to the system characteristics, a new control strategy is designed for the two control variables, and an adaptive integral terminal sliding mode controller is proposed to guarantee the robustness of the system. The simulation results prove the feasibility of the DVHT and show that the control method can achieve multitasking control and effectively improve the control effect.
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Affiliation(s)
- Wei Shen
- Department of Mechatronics Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China; State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310000, China.
| | - Jiehao Wang
- Department of Mechatronics Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
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15
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Zhu Y, Wang J, Li H, Liu C, Grill WM. Adaptive Parameter Modulation of Deep Brain Stimulation Based on Improved Supervisory Algorithm. Front Neurosci 2021; 15:750806. [PMID: 34602976 PMCID: PMC8481598 DOI: 10.3389/fnins.2021.750806] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Accepted: 08/20/2021] [Indexed: 11/23/2022] Open
Abstract
Clinically deployed deep brain stimulation (DBS) for the treatment of Parkinson's disease operates in an open loop with fixed stimulation parameters, and this may result in high energy consumption and suboptimal therapy. The objective of this manuscript is to establish, through simulation in a computational model, a closed-loop control system that can automatically adjust the stimulation parameters to recover normal activity in model neurons. Exaggerated beta band activity is recognized as a hallmark of Parkinson's disease and beta band activity in model neurons of the globus pallidus internus (GPi) was used as the feedback signal to control DBS of the GPi. Traditional proportional controller and proportional-integral controller were not effective in eliminating the error between the target level of beta power and the beta power under Parkinsonian conditions. To overcome the difficulties in tuning the controller parameters and improve tracking performance in the case of changes in the plant, a supervisory control algorithm was implemented by introducing a Radial Basis Function (RBF) network to build the inverse model of the plant. Simulation results show the successful tracking of target beta power in the presence of changes in Parkinsonian state as well as during dynamic changes in the target level of beta power. Our computational study suggests the feasibility of the RBF network-driven supervisory control algorithm for real-time modulation of DBS parameters for the treatment of Parkinson's disease.
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Affiliation(s)
- Yulin Zhu
- Department of Biomedical Engineering, Duke University, Durham, NC, United States
- School of Electrical and Information Engineering, Tianjin University, Tianjin, China
| | - Jiang Wang
- School of Electrical and Information Engineering, Tianjin University, Tianjin, China
| | - Huiyan Li
- School of Automation and Electrical Engineering, Tianjin University of Technology and Education, Tianjin, China
| | - Chen Liu
- School of Electrical and Information Engineering, Tianjin University, Tianjin, China
| | - Warren M. Grill
- Department of Biomedical Engineering, Duke University, Durham, NC, United States
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16
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Song Q, Li S, Bai Q, Yang J, Zhang A, Zhang X, Zhe L. Trajectory Planning of Robot Manipulator Based on RBF Neural Network. Entropy (Basel) 2021; 23:1207. [PMID: 34573832 DOI: 10.3390/e23091207] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/25/2021] [Revised: 09/07/2021] [Accepted: 09/11/2021] [Indexed: 11/17/2022]
Abstract
Robot manipulator trajectory planning is one of the core robot technologies, and the design of controllers can improve the trajectory accuracy of manipulators. However, most of the controllers designed at this stage have not been able to effectively solve the nonlinearity and uncertainty problems of the high degree of freedom manipulators. In order to overcome these problems and improve the trajectory performance of the high degree of freedom manipulators, a manipulator trajectory planning method based on a radial basis function (RBF) neural network is proposed in this work. Firstly, a 6-DOF robot experimental platform was designed and built. Secondly, the overall manipulator trajectory planning framework was designed, which included manipulator kinematics and dynamics and a quintic polynomial interpolation algorithm. Then, an adaptive robust controller based on an RBF neural network was designed to deal with the nonlinearity and uncertainty problems, and Lyapunov theory was used to ensure the stability of the manipulator control system and the convergence of the tracking error. Finally, to test the method, a simulation and experiment were carried out. The simulation results showed that the proposed method improved the response and tracking performance to a certain extent, reduced the adjustment time and chattering, and ensured the smooth operation of the manipulator in the course of trajectory planning. The experimental results verified the effectiveness and feasibility of the method proposed in this paper.
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17
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Yang Q, Ye Z, Li X, Wei D, Chen S, Li Z. Prediction of Flight Status of Logistics UAVs Based on an Information Entropy Radial Basis Function Neural Network. Sensors (Basel) 2021; 21:3651. [PMID: 34073923 DOI: 10.3390/s21113651] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Revised: 05/17/2021] [Accepted: 05/22/2021] [Indexed: 11/24/2022]
Abstract
Aiming at addressing the problems of short battery life, low payload and unmeasured load ratio of logistics Unmanned Aerial Vehicles (UAVs), the Radial Basis Function (RBF) neural network was trained with the flight data of logistics UAV from the Internet of Things to predict the flight status of logistics UAVs. Under the condition that there are few available input samples and the convergence of RBF neural network is not accurate, a dynamic adjustment method of RBF neural network structure based on information entropy is proposed. This method calculates the information entropy of hidden layer neurons and output layer neurons, and quantifies the output information of hidden layer neurons and the interaction information between hidden layer neurons and output layer neurons. The structural design and optimization of RBF neural network were solved by increasing the hidden layer neurons or disconnecting unnecessary connections, according to the connection strength between neurons. The steepest descent learning algorithm was used to correct the parameters of the network structure to ensure the convergence accuracy of the RBF neural network. By predicting the regression values of the flight status of logistics UAVs, it is demonstrated that the information entropy-based RBF neural network proposed in this paper has good approximation ability for the prediction of nonlinear systems.
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18
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Liu W, Zhao T. An active disturbance rejection control for hysteresis compensation based on Neural Networks adaptive control. ISA Trans 2021; 109:81-88. [PMID: 33059906 DOI: 10.1016/j.isatra.2020.10.019] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2019] [Revised: 02/29/2020] [Accepted: 10/04/2020] [Indexed: 06/11/2023]
Abstract
In the present paper, an active disturbance rejection control(ADRC) scheme via radial basis function(RBF) neural networks is designed for adaptive control of non-affine nonlinear systems facing hysteresis disturbance in which RBF neural network approximation is utilized to tackle the system uncertainties and ADRC is designed to real-time estimate and compensate disturbance with unknown backlash-like hysteresis. Combining the adaptive neural networks design with ADRC design techniques, a new dual-channel composite controller scheme is developed herein whereby adaptive neural networks are used as feed-forward inverse control and ADRC as closed-loop feedback control. Furthermore, as compared to adaptive neural networks control algorithm, the proposed RBF-ADRC dual-channel composite controller can guarantee that the desired signal can be tracked with a small domain of the origin and it is confirmed to be effective under Lyapunov stability theory and MATLAB simulations.
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Affiliation(s)
- Wentao Liu
- Automatic Control Department, Qingdao University of Science and Technology, 266061, China.
| | - Tong Zhao
- Automatic Control Department, Qingdao University of Science and Technology, 266061, China.
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19
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Li W, Li M, Qiao J, Guo X. A feature clustering-based adaptive modular neural network for nonlinear system modeling. ISA Trans 2020; 100:185-197. [PMID: 31767196 DOI: 10.1016/j.isatra.2019.11.015] [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: 02/01/2019] [Revised: 08/27/2019] [Accepted: 11/08/2019] [Indexed: 06/10/2023]
Abstract
To improve the performance of nonlinear system modeling, this study proposes a feature clustering-based adaptive modular neural network (FC-AMNN) by simulating information processing mechanism of human brains in the way that different information is processed by different modules in parallel. Firstly, features are clustered using an adaptive feature clustering algorithm, and the number of modules in FC-AMNN is determined by the number of feature clusters automatically. The features in each cluster are then allocated to the corresponding module in FC-AMNN. Then, a self-constructive RBF neural network based on Error Correction algorithm is adopted as the subnetwork to study the allocated features. All modules work in parallel and are finally integrated using a Bayesian method to obtain the output. To demonstrate the effectiveness of the proposed model, FC-AMNN is tested on several UCI benchmark problems as well as a practical problem in wastewater treatment process. The experimental results show that the FC-AMNN can achieve a better generalization performance and an accurate result for nonlinear system modeling compared with other modular neural networks.
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Affiliation(s)
- Wenjing Li
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China; Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing, 100124, China.
| | - Meng Li
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China; Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing, 100124, China
| | - Junfei Qiao
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China; Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing, 100124, China
| | - Xin Guo
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China; Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing, 100124, China
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20
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Abstract
BACKGROUND An important part of the rehabilitation process using exoskeleton robots has been the creation of a friendly Human Robot Interaction (HRI) system. OBJECTIVE In order to combine SEMG signal into the HRI system, a SEMG-angle model based on Hidden Markov Model (HMM) was put forward in this paper. METHODS Feature extraction as a critical issue of signal preprocessing was handled by Principal Component Analysis (PCA) which realized signal data dimension reduction and solved the common problem of redundant features. A comparison study was given to show the different performance of various EMG-angle model separately based on HMM, Back Propagation (BP) neural network and Radial Basis Function (RBF) neural network. RESULTS The HMM modeling method which with lower calculation complexity can achieve a better modeling performance (average accuracy 93.063%) compared with BP neural network (average accuracy 88.180%) and RBF neural network (average accuracy 88.752%). CONCLUSIONS SEMG signals have some characteristic properties which is similar to a quasi-stationary filtered white noise stochastic process, the structure of HMMs makes it ideally suited for classification and modeling SEMG signals, and the results of this study show that it can achieve a better performance than the commonly used methods (BP and RBF).
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Affiliation(s)
- Yanyan Chen
- Lianyungang Jari deepsoft Technology Co., LTD, Lianyungang, Jiangsu 222000, China.,The 716th Research Institute of CSIC, Lianyungang, Jiangsu 222000, China
| | - Le Liang
- Lianyungang Jari deepsoft Technology Co., LTD, Lianyungang, Jiangsu 222000, China.,The 716th Research Institute of CSIC, Lianyungang, Jiangsu 222000, China
| | - Maochuan Wu
- Lianyungang Jari deepsoft Technology Co., LTD, Lianyungang, Jiangsu 222000, China
| | - Qi Dong
- Lianyungang Jari deepsoft Technology Co., LTD, Lianyungang, Jiangsu 222000, China
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21
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Asgharnia A, Jamali A, Shahnazi R, Maheri A. Load mitigation of a class of 5-MW wind turbine with RBF neural network based fractional-order PID controller. ISA Trans 2020; 96:272-286. [PMID: 31326079 DOI: 10.1016/j.isatra.2019.07.006] [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: 07/01/2019] [Accepted: 07/02/2019] [Indexed: 05/16/2023]
Abstract
In variable-pitch wind turbines, pitch angle control is implemented to regulate the rotor speed and power production. However, mechanical loads of the wind turbines are affected by the pitch angle adjustment. To improve the performance and at the same time alleviate the mechanical loads, a gain-scheduling fractional-order PID (FOPID), where a trained RBF neural network chooses its parameters is proposed. The database, which the RBF neural network is trained based on, is created via optimization of a FOPID in several wind speeds with chaotic differential evolution (CDE) algorithm. The simulation results are compared to an RBF based PID controller that is designed via the same method, a conventional gain-scheduling baseline PI controller developed by NREL, an optimal RBF based PI controller, and a FOPI controller. The simulations indicate that the RBF based FOPID improves the control performance of the benchmark wind turbine in comparison to the other controllers, while the applied loads to the structure are mitigated. To validate the performance and robustness, all controllers are implemented on FAST wind turbine simulator. The superiority of the proposed FOPID controller is depicted in comparison to the other controllers.
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Affiliation(s)
- A Asgharnia
- Faculty of Mechanical Engineering, University of Guilan, Rasht, P.O. Box: 3756, Iran.
| | - A Jamali
- Faculty of Mechanical Engineering, University of Guilan, Rasht, P.O. Box: 3756, Iran.
| | - R Shahnazi
- Department of Electrical Engineering, Faculty of Engineering, University of Guilan, Rasht, Iran.
| | - A Maheri
- School of Engineering, University of Aberdeen, Aberdeen AB24 3UE, UK.
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22
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Yang J, Bai JS, Xu Q. An Online Charging Scheme for Wireless Rechargeable Sensor Networks Based on a Radical Basis Function. Sensors (Basel) 2019; 20:E205. [PMID: 31905899 DOI: 10.3390/s20010205] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/08/2019] [Revised: 12/20/2019] [Accepted: 12/26/2019] [Indexed: 11/16/2022]
Abstract
The node energy consumption rate is not dynamically estimated in the online charging schemes of most wireless rechargeable sensor networks, and the charging response of the charging-needed node is fairly poor, which results in nodes easily generating energy holes. Aiming at this problem, an energy hole avoidance online charging scheme (EHAOCS) based on a radical basis function (RBF) neural network, named RBF-EHAOCS, is proposed. The scheme uses the RBF neural network to predict the dynamic energy consumption rate during the charging process, estimates the optimal threshold value of the node charging request on this basis, and then determines the next charging node per the selected conditions: the minimum energy hole rate and the shortest charging latency time. The simulation results show that the proposed method has a lower node energy hole rate and smaller charging node charging latency than two other existing online charging schemes.
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23
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Chen J, Li Q, Wang H, Deng M. A Machine Learning Ensemble Approach Based on Random Forest and Radial Basis Function Neural Network for Risk Evaluation of Regional Flood Disaster: A Case Study of the Yangtze River Delta, China. Int J Environ Res Public Health 2019; 17:E49. [PMID: 31861677 PMCID: PMC6982166 DOI: 10.3390/ijerph17010049] [Citation(s) in RCA: 52] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Revised: 12/07/2019] [Accepted: 12/17/2019] [Indexed: 11/16/2022]
Abstract
The Yangtze River Delta (YRD) is one of the most developed regions in China. This is also a flood-prone area where flood disasters are frequently experienced; the situations between the people-land nexus and the people-water nexus are very complicated. Therefore, the accurate assessment of flood risk is of great significance to regional development. The paper took the YRD urban agglomeration as the research case. The driving force, pressure, state, impact and response (DPSIR) conceptual framework was established to analyze the indexes of flood disasters. The random forest (RF) algorithm was used to screen important indexes of floods risk, and a risk assessment model based on the radial basis function (RBF) neural network was constructed to evaluate the flood risk level in this region from 2009 to 2018. The risk map showed the I-V level of flood risk in the YRD urban agglomeration from 2016 to 2018 by using the geographic information system (GIS). Further analysis indicated that the indexes such as flood season rainfall, urban impervious area ratio, gross domestic product (GDP) per square kilometer of land, water area ratio, population density and emergency rescue capacity of public administration departments have important influence on flood risk. The flood risk has been increasing in the YRD urban agglomeration during the past ten years under the urbanization background, and economic development status showed a significant positive correlation with flood risks. In addition, there were serious differences in the rising rate of flood risks and the status quo among provinces. There are still a few cities that have stabilized at a better flood-risk level through urban flood control measures from 2016 to 2018. These results were basically in line with the actual situation, which validated the effectiveness of the model. Finally, countermeasures and suggestions for reducing the urban flood risk in the YRD region were proposed, in order to provide decision support for flood control, disaster reduction and emergency management in the YRD region.
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Affiliation(s)
- Junfei Chen
- State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China; (H.W.); (M.D.)
- Business School, Hohai University, Nanjing 211100, China;
| | - Qian Li
- Business School, Hohai University, Nanjing 211100, China;
| | - Huimin Wang
- State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China; (H.W.); (M.D.)
- Business School, Hohai University, Nanjing 211100, China;
| | - Menghua Deng
- State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China; (H.W.); (M.D.)
- Business School, Hohai University, Nanjing 211100, China;
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24
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Sheng G, Gao G, Zhang B. Application of Improved Wavelet Thresholding Method and an RBF Network in the Error Compensating of an MEMS Gyroscope. Micromachines (Basel) 2019; 10:mi10090608. [PMID: 31540303 PMCID: PMC6780760 DOI: 10.3390/mi10090608] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/23/2019] [Revised: 09/08/2019] [Accepted: 09/12/2019] [Indexed: 11/16/2022]
Abstract
The large random errors in Micro-Electro-Mechanical System (MEMS) gyros are one of the major factors that affect the precision of inertial navigation systems. Based on the indoor inertial navigation system, an improved wavelet threshold de-noising method was proposed and combined with a gradient radial basis function (RBF) neural network to better compensate errors. We analyzed the random errors in an MEMS gyroscope by using Allan variance, and introduced the traditional wavelet threshold methods. Then, we improved the methods and proposed a new threshold function. The new method can be used more effectively to detach white noise and drift error in the error model. Finally, the drift data was modeled and analyzed in combination with the RBF neural network. Experimental results indicate that the method is effective, and this is of great significance for improving the accuracy of indoor inertial navigation based on MEMS gyroscopes.
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Affiliation(s)
- Guangrun Sheng
- Beijing Key Laboratory of Sensors, Beijing Information Science & Technology University, Beijing 100101, China.
| | - Guowei Gao
- Beijing Key Laboratory of Sensors, Beijing Information Science & Technology University, Beijing 100101, China.
- Key Laboratory of Modern Measurement & Control Technology, Ministry of Education, Beijing Information Science & Technology University, Beijing 100192, China.
| | - Boyuan Zhang
- Beijing Key Laboratory of Sensors, Beijing Information Science & Technology University, Beijing 100101, China.
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25
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Miao T, Lu W, Guo J, Lin J, Fan Y. Modeling and uncertainty analysis of seawater intrusion based on surrogate models. Environ Sci Pollut Res Int 2019; 26:26015-26025. [PMID: 31273667 DOI: 10.1007/s11356-019-05799-3] [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] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/06/2019] [Accepted: 06/19/2019] [Indexed: 06/09/2023]
Abstract
When using a simulation model to study seawater intrusion (SI), uncertainty in the parameters directly affects the results. The impact of the rise in sea levels due to global warming on SI cannot be ignored. In this paper, the Monte Carlo method is used to analyze the uncertainty in modeling SI. To reduce the computational cost of the repeated invocation of the simulation model as well as time, a surrogate model is established using a radial basis function (RBF)-based neural network method. To enhance the accuracy of the substitution model, input samples are sampled using the Latin hypercube sampling (LHS) method. The results of uncertainty analysis had a high reference value and show the following: (1) The surrogate model created using the RBF method can significantly reduce computational cost and save at least 95% of the time needed for the repeated invocation of the simulation model while maintaining high accuracy. (2) Uncertainty in the parameters and the magnitude of the rise in sea levels have a significant impact on SI. The results of prediction were thus highly uncertain. In practice, it is necessary to quantify uncertainty to provide more intuitive predictions.
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Affiliation(s)
- Tiansheng Miao
- College of New Energy and Environment, Jilin University, Changchun, 130021, China
- Key Laboratory of Groundwater Resources and Environment (Jilin University), Ministry of Education, Changchun, 130021, China
- Jilin Provincial Key Laboratory of Water Resources and Environment, Jilin University, Changchun, 130021, China
| | - Wenxi Lu
- College of New Energy and Environment, Jilin University, Changchun, 130021, China.
- Key Laboratory of Groundwater Resources and Environment (Jilin University), Ministry of Education, Changchun, 130021, China.
- Jilin Provincial Key Laboratory of Water Resources and Environment, Jilin University, Changchun, 130021, China.
| | - Jiayuan Guo
- College of New Energy and Environment, Jilin University, Changchun, 130021, China
- Key Laboratory of Groundwater Resources and Environment (Jilin University), Ministry of Education, Changchun, 130021, China
- Jilin Provincial Key Laboratory of Water Resources and Environment, Jilin University, Changchun, 130021, China
| | - Jin Lin
- Nanjing Hydraulic Research Institute, Nanjing, 210029, China
| | - Yue Fan
- College of New Energy and Environment, Jilin University, Changchun, 130021, China
- Key Laboratory of Groundwater Resources and Environment (Jilin University), Ministry of Education, Changchun, 130021, China
- Jilin Provincial Key Laboratory of Water Resources and Environment, Jilin University, Changchun, 130021, China
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26
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Zhang Y, Ye J, Lin Z, Huang S, Wang H, Wu H. A Piezoresistive Tactile Sensor for a Large Area Employing Neural Network. Sensors (Basel) 2018; 19:E27. [PMID: 30577675 DOI: 10.3390/s19010027] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/06/2018] [Revised: 12/15/2018] [Accepted: 12/17/2018] [Indexed: 02/04/2023]
Abstract
Electronic skin is an important means through which robots can obtain external information. A novel flexible tactile sensor capable of simultaneously detecting the contact position and force was proposed in this paper. The tactile sensor had a three-layer structure. The upper layer was a specially designed conductive film based on indium-tin oxide polyethylene terephthalate (ITO-PET), which could be used for detecting contact position. The intermediate layer was a piezoresistive film used as the force-sensitive element. The lower layer was made of fully conductive material such as aluminum foil and was used only for signal output. In order to solve the inconsistencies and nonlinearity of the piezoresistive properties for large areas, a Radial Basis Function (RBF) neural network was used. This includes input, hidden, and output layers. The input layer has three nodes representing position coordinates, X, Y, and resistor, R. The output layer has one node representing force, F. A sensor sample was fabricated and experiments of contact position and force detection were performed on the sample. The results showed that the principal function of the tactile sensor was feasible. The sensor sample exhibited good consistency and linearity. The tactile sensor has only five lead wires and can provide the information support necessary for safe human—computer interactions.
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27
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Deng M, Wang C, Tang M, Zheng T. Extracting cardiac dynamics within ECG signal for human identification and cardiovascular diseases classification. Neural Netw 2018; 100:70-83. [PMID: 29471197 DOI: 10.1016/j.neunet.2018.01.009] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2017] [Revised: 01/15/2018] [Accepted: 01/19/2018] [Indexed: 11/24/2022]
Abstract
Cardiac characteristics underlying the time/frequency domain features are limited and not comprehensive enough to reflect the temporal/dynamical nature of ECG patterns. This paper proposes a dynamical ECG recognition framework for human identification and cardiovascular diseases classification via a dynamical neural learning mechanism. The proposed method consists of two phases: a training phase and a test phase. In the training phase, cardiac dynamics within ECG signals is extracted (approximated) accurately by using radial basis function (RBF) neural networks through deterministic learning mechanism. The obtained cardiac system dynamics is represented and stored in constant RBF networks. An ECG signature is then derived from the extracted cardiac dynamics along the periodic ECG state trajectories. A bank of estimators is constructed using the extracted cardiac dynamics to represent the trained gait patterns. In the test phase, recognition errors are generated and taken as the similarity measure by comparing the cardiac dynamics of the trained ECG patterns and the dynamics of the test ECG pattern. Rapid recognition of a test ECG pattern begins with measuring the state of test pattern, and automatically proceeds with the evolution of the recognition error system. According to the smallest error principle, the test ECG pattern can be rapidly recognized. This kind of cardiac dynamics information represents the beat-to-beat temporal change of ECG modifications and the temporal/dynamical nature of ECG patterns. Therefore, the amount of discriminability provided by the cardiac dynamics is larger than the original signals. This paper further discusses the extension of the proposed method for cardiovascular diseases classification. The constructed recognition system can distinguish and assign dynamical ECG patterns to predefined classes according to the similarity of cardiac dynamics. Experiments are carried out on the FuWai and PTB ECG databases to demonstrate the effectiveness of the proposed method.
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Affiliation(s)
- Muqing Deng
- Institute of Information and Control, Hangzhou Dianzi University, Hangzhou 310018, China.
| | - Cong Wang
- College of Automation, South China University of Technology, Guangzhou 510640, China.
| | - Min Tang
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100000, China
| | - Tongjia Zheng
- College of Engineering, University of Notre Dame, Notre Dame, IN 46556, USA
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28
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Zeng W, Wang Q, Liu F, Wang Y. Learning from adaptive neural network output feedback control of a unicycle-type mobile robot. ISA Trans 2016; 61:337-347. [PMID: 26830003 DOI: 10.1016/j.isatra.2016.01.005] [Citation(s) in RCA: 8] [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: 05/26/2015] [Revised: 11/14/2015] [Accepted: 01/11/2016] [Indexed: 06/05/2023]
Abstract
This paper studies learning from adaptive neural network (NN) output feedback control of nonholonomic unicycle-type mobile robots. The major difficulties are caused by the unknown robot system dynamics and the unmeasurable states. To overcome these difficulties, a new adaptive control scheme is proposed including designing a new adaptive NN output feedback controller and two high-gain observers. It is shown that the stability of the closed-loop robot system and the convergence of tracking errors are guaranteed. The unknown robot system dynamics can be approximated by radial basis function NNs. When repeating same or similar control tasks, the learned knowledge can be recalled and reused to achieve guaranteed stability and better control performance, thereby avoiding the tremendous repeated training process of NNs.
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Affiliation(s)
- Wei Zeng
- School of Mechanical & Electrical Engineering, Longyan University, Longyan 364012, China.
| | - Qinghui Wang
- School of Mechanical & Electrical Engineering, Longyan University, Longyan 364012, China
| | - Fenglin Liu
- School of Mechanical & Electrical Engineering, Longyan University, Longyan 364012, China
| | - Ying Wang
- School of Mechanical & Electrical Engineering, Longyan University, Longyan 364012, China
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29
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Shirzadeh M, Amirkhani A, Jalali A, Mosavi MR. An indirect adaptive neural control of a visual-based quadrotor robot for pursuing a moving target. ISA Trans 2015; 59:290-302. [PMID: 26521725 DOI: 10.1016/j.isatra.2015.10.011] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [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: 03/23/2015] [Revised: 09/07/2015] [Accepted: 10/13/2015] [Indexed: 06/05/2023]
Abstract
This paper aims to use a visual-based control mechanism to control a quadrotor type aerial robot which is in pursuit of a moving target. The nonlinear nature of a quadrotor, on the one hand, and the difficulty of obtaining an exact model for it, on the other hand, constitute two serious challenges in designing a controller for this UAV. A potential solution for such problems is the use of intelligent control methods such as those that rely on artificial neural networks and other similar approaches. In addition to the two mentioned problems, another problem that emerges due to the moving nature of a target is the uncertainty that exists in the target image. By employing an artificial neural network with a Radial Basis Function (RBF) an indirect adaptive neural controller has been designed for a quadrotor robot in search of a moving target. The results of the simulation for different paths show that the quadrotor has efficiently tracked the moving target.
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Affiliation(s)
- Masoud Shirzadeh
- Department of Electrical Engineering, Iran University of Science and Technology, Tehran 16846-13114, Iran.
| | - Abdollah Amirkhani
- Department of Electrical Engineering, Iran University of Science and Technology, Tehran 16846-13114, Iran.
| | - Aliakbar Jalali
- Department of Electrical Engineering, Iran University of Science and Technology, Tehran 16846-13114, Iran
| | - Mohammad R Mosavi
- Department of Electrical Engineering, Iran University of Science and Technology, Tehran 16846-13114, Iran
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Abstract
This work was aimed at studying the method of computer-aided diagnosis of early knee OA (OA: osteoarthritis). Based on the technique of MRI (MRI: Magnetic Resonance Imaging) T2 Mapping, through computer image processing, feature extraction, calculation and analysis via constructing a classifier, an effective computer-aided diagnosis method for knee OA was created to assist doctors in their accurate, timely and convenient detection of potential risk of OA. In order to evaluate this method, a total of 1380 data from the MRI images of 46 samples of knee joints were collected. These data were then modeled through linear regression on an offline general platform by the use of the ImageJ software, and a map of the physical parameter T2 was reconstructed. After the image processing, the T2 values of ten regions in the WORMS (WORMS: Whole-organ Magnetic Resonance Imaging Score) areas of the articular cartilage were extracted to be used as the eigenvalues in data mining. Then,a RBF (RBF: Radical Basis Function) network classifier was built to classify and identify the collected data. The classifier exhibited a final identification accuracy of 75%, indicating a good result of assisting diagnosis. Since the knee OA classifier constituted by a weights-directly-determined RBF neural network didn't require any iteration, our results demonstrated that the optimal weights, appropriate center and variance could be yielded through simple procedures. Furthermore, the accuracy for both the training samples and the testing samples from the normal group could reach 100%. Finally, the classifier was superior both in time efficiency and classification performance to the frequently used classifiers based on iterative learning. Thus it was suitable to be used as an aid to computer-aided diagnosis of early knee OA.
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Affiliation(s)
- Yixiao Wu
- Department of Medical Equipment, The Fifth Affiliated Hospital of Sun Yat-Sen University, Zhuhai, P.R. China
| | - Ran Yang
- School of Mobile Information Engineering, Sun Yat-Sen University, Zhuhai, P.R. China
| | - Sen Jia
- School of Information Science and Technology, Sun Yat-Sen University, Guangzhou, P.R. China
| | - Zhanjun Li
- Department of Medical Equipment, The Fifth Affiliated Hospital of Sun Yat-Sen University, Zhuhai, P.R. China
| | - Zhiyang Zhou
- Department of Medical Imaging, The Sixth Affiliated Hospital of Sun Yat-Sen University, Guangzhou, P.R. China
| | - Ting Lou
- Department of Neurosurgery, The Fifth Affiliated Hospital of Sun Yat-Sen University, Zhuhai, P.R. China
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