A resource-aware sliding mode control approach for Markov jump systems

The problem of sliding mode control (SMC) for a class of Markov jump systems (MJSs) is addressed in this paper based on a resource-aware triggering mechanism which realizes computational resources saving and disturbance attenuation simultaneously. By introducing the self-triggered policy, the next execution time is pre-computed for sampling, updating and executing by relying on the latest sampled information. Then, the switching surface and the related dynamics of the original MJSs are obtained by means of a self-triggered sampling scheme. To guarantee both the system stability and the desired disturbance attenuation performance, sufficient conditions are presented in terms of linear matrix inequalities. Moreover, to ensure the time finiteness of the predefined switching surface reachability and satisfy the desirable sliding motion performance, an SMC law is proposed. The validity and superiority of the developed scheme are demonstrated via a simulation example.

A Fuzzy Fusion Rotating Machinery Fault Diagnosis Framework Based on the Enhancement Deep Convolutional Neural Networks

Some artificial intelligence algorithms have gained much attention in the rotating machinery fault diagnosis due to their robust nonlinear regression properties. In addition, existing deep learning algorithms are usually dependent on single signal features, which would lead to the loss of some information or incomplete use of the information in the signal. To address this problem, three kinds of popular signal processing methods, including Fast Fourier Transform (FFT), Short-Time Fourier Transform (STFT) and directly slicing one-dimensional data into the two-dimensional matrix, are used to create four different datasets from raw vibration signal as the input data of four enhancement Convolutional Neural Networks (CNN) models. Then, a fuzzy fusion strategy is used to fuse the output of four CNN models that could analyze the importance of each classifier and explore the interaction index between each classifier, which is different from conventional fusion strategies. To show the performance of the proposed model, an artificial fault bearing dataset and a real-world bearing dataset are used to test the feature extraction capability of the model. The good anti-noise and interpretation characteristics of the proposed method are demonstrated as well.

Observer-based adaptive neural tracking control for a class of nonlinear systems with prescribed performance and input dead-zone constraints

This paper investigates the problem of output feedback neural network (NN) learning tracking control for nonlinear strict feedback systems subject to prescribed performance and input dead-zone constraints. First, an NN is utilized to approximate the unknown nonlinear functions, then a state observer is developed to estimate the unmeasurable states. Second, based on the command filter method, an output feedback NN learning backstepping control algorithm is established. Third, a prescribed performance function is employed to ensure the transient performance of the closed-loop systems and forces the tracking error to fall within the prescribed performance boundary. It is rigorously proved mathematically that all the signals in the closed-loop systems are semi-globally uniformly ultimately bounded and the tracking error can converge to an arbitrarily small neighborhood of the origin. Finally, a numerical example and an application example of the electromechanical system are given to show effectiveness of the acquired control algorithm.

Finite- and Fixed-Time Cluster Synchronization of Nonlinearly Coupled Delayed Neural Networks via Pinning Control

In this article, the cluster synchronization problem for a class of the nonlinearly coupled delayed neural networks (NNs) in both finite- and fixed-time cases are investigated. Based on the Lyapunov stability theory and pinning control strategy, some criteria are provided to ensure the cluster synchronization of the nonlinearly coupled delayed NNs in both finite-and fixed-time aspects. Then, the settling time for stabilization that is dependent on the initial value and independent of the initial value is estimated, respectively. Finally, we illustrate the feasibility and practicality of the results via a numerical example.

Output Multiformation Tracking of Networked Heterogeneous Robotic Systems via Finite-Time Hierarchical Control

This article investigates the finite-time output multiformation tracking (OMFT) problem of networked heterogeneous robotic systems (NHRSs), where each robot model involves external disturbances, parametric uncertainties, and possible kinematic redundancy. Besides, the interactions among robotic systems are described as a directed graph with an acyclic partition. Then, several novel practical finite-time hierarchical control (FTHC) algorithms are designed. The convergence analysis of the closed-loop dynamics is extremely difficult due to the lack of effective analysis methods. Based on the mathematics induction and reductio ad absurdum, a new nonsmooth Lyapunov function is proposed to derive the sufficient conditions and settling time functions. Finally, numerical simulations are performed on the NHRS to verify the main results.

Residual wide-kernel deep convolutional auto-encoder for intelligent rotating machinery fault diagnosis with limited samples

This paper deals with the development of a novel deep learning framework to achieve highly accurate rotating machinery fault diagnosis using residual wide-kernel deep convolutional auto-encoder. Unlike most existing methods, in which the input data is processed by fast Fourier transform (FFT) and wavelet transform, this paper aims to learn important features from limited raw vibration signals. Firstly, the wide-kernel convolutional layer is introduced in the convolutional auto-encoder that can ensure the model can learn effective features from the data without any signal processing. Secondly, the residual learning block is introduced in convolutional auto-encoder that can ensure the model with sufficient depth without gradient vanishing and overfitting problems. Thirdly, convolutional auto-encoder can learn constructive features without massive data. To evaluate the performance of the proposed model, Case Western Reserve University (CWRU) bearing dataset and Southeast University (SEU) gearbox dataset are used to test. The experiment results and comparisons verify the denoising and feature extraction ability of the proposed model in the case of very few training samples.

Mutual-Collision-Avoidance Scheme Synthesized by Neural Networks for Dual Redundant Robot Manipulators Executing Cooperative Tasks

Collision between dual robot manipulators during working process will lead to task failure and even robot damage. To avoid mutual collision of dual robot manipulators while doing collaboration tasks, a novel recurrent neural network (RNN)-based mutual-collision-avoidance (MCA) scheme for solving the motion planning problem of dual manipulators is proposed and exploited. Because of the high accuracy and low computation complexity, the linear variational inequality-based primal-dual neural network is used to solve the proposed scheme. The proposed scheme is applied to the collaboration trajectory tracking and cup-stacking tasks, and shows its effectiveness for avoiding collision between the dual robot manipulators. Through network iteration and online learning, the dual robot manipulators will learn the ability of MCA. Moreover, a line-segment-based distance measure algorithm is proposed to calculate the minimum distance between the dual manipulators. If the computed minimum distance is less than the first safe-related distance threshold, a speed brake operation is executed and guarantees that the robot cannot exceed the second safe-related distance threshold. Furthermore, the proposed MCA strategy is formulated as a standard quadratic programming problem, which is further solved by an RNN. Computer simulations and a real dual robot experiment further verify the effectiveness, accuracy, and physical realizability of the RNN-based MCA scheme when manipulators cooperatively execute the end-effector tasks.

Event-Triggered Communication and Annular Finite-Time H∞ Filtering for Networked Switched Systems

Event-triggered communication mechanism (ETCM) provides an efficient way to reduce unwanted network traffic. This article studies the co-design of an ETCM and an annular finite-time (AFT) H_∞ filter for networked switched systems (NSSs). First, the AFT definition and ETCM are presented. Second, a set of mode-dependent average dwell-time (MADT) switching rules is given. By resorting to a delay-dependent Lyapunov functional approach, some feasible AFT H_∞ filters are designed. Third, it is proved that the filtering error system (FES) has a good performance in attenuating the external disturbances. Finally, the feasibility of the developed method is verified via simulation.

Event-Based Secure Leader-Following Consensus Control for Multiagent Systems With Multiple Cyber Attacks

This article concentrates on event-based secure leader-following consensus control for multiagent systems (MASs) with multiple cyber attacks, which contain replay attacks and denial-of-service (DoS) attacks. A new multiple cyber-attacks model is first built by considering replay attacks and DoS attacks simultaneously. Different from the existing researches on MASs with a fixed topological graph, the changes of communication topologies caused by DoS attacks are considered for MASs. Besides, an event-triggered mechanism is adopted for mitigating a load of network bandwidth by scheduling the transmission of sampled data. Then, an event-based consensus control protocol is first developed for MASs subjected to multiple cyber attacks. In view of this, by using the Lyapunov stability theory, sufficient conditions are obtained to ensure the mean-square exponential consensus of MASs. Furthermore, the event-based controller gain is derived by solving a set of linear matrix inequalities. Finally, an example is simulated for confirming the effectiveness of the theoretical results.

Finite-Time L2-Gain Asynchronous Control for Continuous-Time Positive Hidden Markov Jump Systems via T-S Fuzzy Model Approach

This article investigates the finite-time asynchronous control problem for continuous-time positive hidden Markov jump systems (HMJSs) by using the Takagi-Sugeno fuzzy model method. Different from the existing methods, the Markov jump systems under consideration are considered with the hidden Markov model in the continuous-time case, that is, the Markov model consists of the hidden state and the observed state. We aim to derive a suitable controller that depends on the observation mode which makes the closed-loop fuzzy HMJSs be stochastically finite-time bounded and positive, and fulfill the given L₂ performance index. Applying the stochastic Lyapunov-Krasovskii functional (SLKF) methods, we establish sufficient conditions to obtain the finite-time state-feedback controller. Finally, a Lotka-Volterra population model is used to show the feasibility and validity of the main results.

Input-Output Finite-Time Generalized Dissipative Filter of Discrete Time-Varying Systems With Quantization and Adaptive Event-Triggered Mechanism

This article discusses the issue of input-output finite-time generalized dissipative filter design for a class of discrete time-varying systems. First, an adaptive event-triggered mechanism (AETM) with an adaptive law is proposed to adjust the threshold in the AETM according to the error between the system states and the filter states. Such an AETM determines whether the measurement output should be transmitted or not, which is more effective to economize the communication resources comparing with the traditional event-triggered mechanism. Second, in view of network-induced delays, the quantization and the AETM, a time-varying filter error system (TV-FES) is modeled. Then, a new augmented time-varying Lyapunov functional containing triple sum terms is provided. Based on the new finite-sum inequality and improved reciprocally convex combination lemma, delay-dependent conditions are obtained, which can ensure the TV-FES to be input-output finite-time stable and satisfy the given generalized dissipative performance. Moreover, the recursive linear matrix inequalities are presented to obtain the desired filter gains. Finally, numerical examples demonstrate the superiority and feasibility of the proposed method in this article.

A Deterministic Annealing Neural Network Algorithm for the Minimum Concave Cost Transportation Problem

In this article, a deterministic annealing neural network algorithm is proposed to solve the minimum concave cost transportation problem. Specifically, the algorithm is derived from two neural network models and Lagrange-barrier functions. The Lagrange function is used to handle linear equality constraints, and the barrier function is used to force the solution to move to the global or near-global optimal solution. In both neural network models, two descent directions are constructed, and an iterative procedure for the optimization of the neural network is proposed. As a result, two corresponding Lyapunov functions are naturally obtained from these two descent directions. Furthermore, the proposed neural network models are proved to be completely stable and converge to the stable equilibrium state, therefore, the proposed algorithm converges. At last, the computer simulations on several test problems are made, and the results indicate that the proposed algorithm always generates global or near-global optimal solutions.

Fuzzy Output Tracking Control and Filtering for Nonlinear Discrete-Time Descriptor Systems Under Unreliable Communication Links

In this paper, the problems of output tracking control and filtering are investigated for Takagi-Sugeno fuzzy-approximation-based nonlinear descriptor systems in the discrete-time domain. Especially, the unreliability of the communication links between the sensor and actuator/filter is taken into account, and the phenomenon of packet dropouts is characterized by a binary Markov chain with uncertain transition probabilities, which may reflect the reality more accurately than the existing description processes. A novel bounded real lemma (BRL), which ensures the stochastic admissibility with H_∞ performance for fuzzy discrete-time descriptor systems despite the uncertain Markov packet dropouts, is presented based on a fuzzy basis-dependent Lyapunov function. By resorting to the dual conditions of the obtained BRL, a solution for the designed fuzzy output tracking controller is given. A design method for the full-order fuzzy filter is also provided. Finally, two examples are finally adopted to show the applicability of the achieved design strategies.

Adaptive Control of Nonlinear Semi-Markovian Jump T-S Fuzzy Systems With Immeasurable Premise Variables via Sliding Mode Observer

The issue of observer-based adaptive sliding mode control of nonlinear Takagi-Sugeno fuzzy systems with semi-Markov switching and immeasurable premise variables is investigated. More general nonlinear systems are described in the model since the selections of premise variables are the states of the system. First, a novel integral sliding surface function is proposed on the observer space, then the sliding mode dynamics and error dynamics are obtained in accordance with estimated premise variables. Second, sufficient conditions for stochastic stability with an H_∞ performance disturbance attenuation level γ of the sliding mode dynamics with different input matrices are obtained based on generally uncertain transition rates. Third, an observer-based adaptive controller is synthesized to ensure the finite time reachability of a predefined sliding surface. Finally, the single-link robot arm model is provided to verify the control scheme numerically.

Effects of Passive- and Active-Modified Atmosphere Packaging on Physio-Chemical and Quality Attributes of Fresh In-Hull Pistachios (Pistacia vera L. cv. Badami)

The effects of passive- and active-modified atmosphere packaging (passive- and active-MAP) were investigated on the physio-chemical and quality attributes of fresh in-hull pistachios stored at 4 ± 1 °C and 90 ± 5% R.H. Fresh pistachios were packaged under each of the following gas combinations: active-MAP1 (AMA1) (5% O2 + 5% CO2), AMA2 (5% O2 + 25% CO2), AMA3 (5% O2 + 45% CO2), AMA4 (2.5% O2 + 5% CO2), AMA5 (2.5% O2 + 25% CO2), and AMA6 (2.5% O2 + 45% CO2), all balanced with N2, as well as passive-MAP (PMA) with ambient air (21% O2 + 0.03% CO2 + 78% N2). Changes in quality parameters were evaluated after 0, 15, 30 and 45 days of storage. Results demonstrated that AMA6 and PMA had significantly lower (7.96 Log CFU g-1) and higher (9.81 Log CFU g-1) aerobic mesophilic bacteria counts than the other treatments. However, the AMA6 treatment decreased, kernel chlorophyll and carotenoid content, hull antioxidant capacity, and anthocyanin content. The PMA treatment produced a significant weight loss, 0.18%, relative to the other treatments. The active-MAP treatments were more effective than the passive-MAP in decreasing weight loss, microbial counts, kernel total chlorophyll (Kernel TCL), and kernel carotenoid content (Kernel CAC). The postharvest quality of fresh in-hull pistachios was maintained best by the AMA3 (5% O2 + 45% CO2 + 50% N2) treatment.

Region Stabilization of Switched Neural Networks With Multiple Modes and Multiple Equilibria: A Pole Assignment Method

This article investigates region stabilization issue of switched neural networks (SNNs) with multiple modes (MMs) and multiple equilibria (ME) via a pole assignment method. In such an SNN, every neuron is observed with more than one mode and unstable equilibrium point. First, SNNs with MMs and ME are modeled in terms of switched systems with unstable subsystems and ME. Second, a necessary and sufficient condition and a sufficient condition are, respectively, proposed for arbitrary switching paths pole assignment and arbitrary periodic/quasi-periodic switching paths (PSPs/QSPs) asymptotically region stabilizing pole assignment of switched linear time-invariant (LTI) systems with ME. It is shown that to stabilize a switched LTI system, some/all poles of all/some linear subsystems can be assigned to suitable locations of the right-half side of the complex plane. Third, based on the obtained pole assignment results, an asymptotical-region-stabilizing-control law observed as distributed state feedback controllers of MMs, asymptotical-region-stabilizing PSPs/QSPs, and a corresponding algorithm are all designed for asymptotical region stabilization of switched linear/nonlinear neural networks with MMs and ME. Finally, a numeral example is given to illustrate the effectiveness and practicality of the new results.

Fuzzy-Model-Based Sliding Mode Control of Nonlinear Descriptor Systems

This paper addresses the problem of sliding mode control (SMC) for a type of uncertain time-delay nonlinear descriptor systems represented by T-S fuzzy models. One crucial contributing factor is to put forward a novel integral fuzzy switching manifold involved with time delay. Compared with previous results, the key benefit of the new manifold is that the input matrices via different subsystems are permitted to be diverse, and thus much more applicability will be achieved. By resorting to Frobenius' theorem and double orthogonal complement, the existence condition of the fuzzy manifold is presented. The admissibility conditions of sliding motion with a strictly dissipative performance are further provided. Then, the desired fuzzy SMC controller is synthesized by analyzing the reachability of the manifold. Moreover, an adaptive fuzzy SMC controller is also proposed to adapt the input saturation and the matched uncertainty with unknown upper bounds. The feasibility and virtue of our theoretical findings are demonstrated by a fuzzy SMC controller implementation for a practical system about the pendulum.

An approximation algorithm for graph partitioning via deterministic annealing neural network

Graph partitioning, a classical NP-hard combinatorial optimization problem, is widely applied to industrial or management problems. In this study, an approximated solution of the graph partitioning problem is obtained by using a deterministic annealing neural network algorithm. The algorithm is a continuation method that attempts to obtain a high-quality solution by following a path of minimum points of a barrier problem as the barrier parameter is reduced from a sufficiently large positive number to 0. With the barrier parameter assumed to be any positive number, one minimum solution of the barrier problem can be found by the algorithm in a feasible descent direction. With a globally convergent iterative procedure, the feasible descent direction could be obtained by renewing Lagrange multipliers red. A distinctive feature of it is that the upper and lower bounds on the variables will be automatically satisfied on the condition that the step length is a value from 0 to 1. Four well-known algorithms are compared with the proposed one on 100 test samples. Simulation results show effectiveness of the proposed algorithm.

A data-driven ground fault detection and isolation method for main circuit in railway electrical traction system

Due to the complex and harsh operation conditions, like corrosion, aging cable and static electricity, of electrical traction drive system, ground fault will generate large short circuit current to harm the key components. Effective fault diagnosis is important, but also challenging. The conventional method used for ground fault detection only takes advantage of voltage measurements of DC-link. Other measurements onboard are also available, which are correlated with the voltage measurements. Taking the correlation into account will improve the detection performance. To this end, this paper presents a data-driven solution, which makes full use of the correlation between the voltage measurements with other measurements onboard. The proposed method consists of two components: (1) a canonical correlation analysis-based fault detection method, which takes into account the correlation within measurements; (2) a fault isolation method by means of the fault direction, which can be obtained with the available faulty data stored in the long-term operation. The developed method is applied to a traction drive system. It is shown that the proposed approach is able to improve the fault detection and isolation performance significantly with respect to three performance indicators, namely fault detection rate, detection delay and correct isolation rate, in comparison with the conventional method, which only uses the voltage measurements of DC-link.