Adaptive Event-Triggered Fault Detection Scheme for Semi-Markovian Jump Systems With Output Quantization

Zhang, Linchuang; Liang, Hongjing; Sun, Yonghui; Ahn, Choon Ki

doi:10.1109/tsmc.2019.2912846

Cited by 203 publications

(78 citation statements)

References 61 publications

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“…Also, this is inspired by the construction of Lyapunov function for ordinary differential systems. At present, Lyapunov method is still a simple and effective way to study the stability and stabilization of some semi-Markov jump systems or other systems, [34][35][36] and how to design an optimal functional to obtain a less conservative result is an interesting topic.…”

Section: Asymptotic Stabilization For Smrdssmentioning

confidence: 99%

Boundary control of stochastic reaction‐diffusion systems with Markovian switching

Han

Ding

et al. 2020

Intl J Robust & Nonlinear

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This article focuses on the boundary control of stochastic Markovian reaction-diffusion systems (SMRDSs). Both the cases of completely known and partially unknown transition probabilities are taken into account. By using the Lyapunov functional method, a sufficient condition is obtained under the designed boundary controllers to guarantee the asymptotic mean square stability for SMRDSs with completely known transition probabilities. For the case of partially unknown transition probabilities, we introduce free-connection weighting matrices to handle the boundary control problem. When external disturbance enters the system, a sufficient criterion of H-infinity boundary control is developed. Furthermore, robust stabilization is investigated for parametric uncertain SMRDSs in both cases. Two examples are presented to demonstrate the efficiency of the proposed approaches. K E Y W O R D S asymptotic stability, boundary control, H-infinity control, partially unknown transition probabilities, stochastic Markovian reaction-diffusion systems Int J Robust Nonlinear Control. 2020;30:4129-4148.wileyonlinelibrary.com/journal/rnc

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Section: Asymptotic Stabilization For Smrdssmentioning

confidence: 99%

Boundary control of stochastic reaction‐diffusion systems with Markovian switching

Han

Ding

et al. 2020

Intl J Robust & Nonlinear

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“…As an important method to reduce the frequency of signal transmission between systems and controllers, the event-triggered control has important theoretical and practical significance. For the first time, the superiority of event-triggered control has been illustrated in reducing information transmission and occupying communication bandwidth [20][21][22][23][24]. Meanwhile, in [25], it was stated that many control systems are more suitable for variable period sampling.…”

Section: Introductionmentioning

confidence: 99%

Adaptive event-triggered control for a class of nonlinear systems with periodic disturbances

et al. 2020

Sci. China Inf. Sci.

246

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This paper investigates the adaptive event-triggered control problem for a class of nonlinear systems subject to periodic disturbances. To reduce the communication burden, a reliable relative threshold strategy is proposed. Fourier series expansion and radial basis function neural network are combined into a function approximator to model suitable time-varying disturbed function of known periods in strict-feedback systems. By combining the Lyapunov stability theory and the backstepping technique, the proposed adaptive control approach ensures that all the signals in the closed-loop system are bounded, and the tracking error can be regulated to a compact set around zero in finite time. Finally, simulation results are presented to verify the effectiveness of the theoretical results.

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“…In another aspect, quantization generally exists in lots of controlled nonlinear systems, where the system is interacted with quantized information, a quantized control approach possesses sufficient precision and requires low communication rate. Many excellent results have been published, for example, the investigation of uncertain nonlinear system with input quantization was done in Reference , and an adaptive asymptotic tracking control approach was established. In Reference , the finite‐time output‐feedback controller was constructed for quantized nonlinear systems.…”

Section: Introductionmentioning

confidence: 99%

Asymptotic tracking control for constrained nonstrict‐feedback MIMO nonlinear systems via parameter compensations

Pan

Chadli

et al. 2020

Intl J Robust & Nonlinear

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This paper studies the problem of adaptive fuzzy asymptotic tracking control for multiple input multiple output nonlinear systems in nonstrict-feedback form.Full state constraints, input quantization, and unknown control direction are simultaneously considered in the systems. By using the fuzzy logic systems, the unknown nonlinear functions are identified. A modified partition of variables is introduced to handle the difficulty caused by nonstrict-feedback structure. In each step of the backstepping design, the symmetric barrier Lyapunov functions are designed to avoid the breach of the state constraints, and the issues of overparametrization and unknown control direction are settled via introducing two compensation functions and the property of Nussbaum function, respectively. Furthermore, an adaptive fuzzy asymptotic tracking control strategy is raised.Based on Lyapunov stability analysis, the developed control strategy can effectually ensure that all the system variables are bounded, and the tracking errors asymptotically converge to zero. Eventually, simulation results are supplied to verify the feasibility of the proposed scheme. K E Y W O R D Sadaptive fuzzy control, asymptotic tracking control, nonstrict-feedback system, state constraints, unknown control direction INTRODUCTIONOn account of the extensively practical applications, studies on adaptive trajectory tracking control design for uncertain nonlinear systems have made a major breakthrough, such as adaptive control, 1-3 sliding mode control, 4-6 robust control, 7-9 fault tolerant control, 10,11 and so on. In addition, backstepping-based adaptive neural control 12-18 and fuzzy control [19][20][21][22][23][24][25][26][27][28][29][30] for nonlinear systems have been well studied in the past few years, in which fuzzy logic systems (FLSs) or neural networks (NNs) were viewed as universal approximators to identify the uncertain system nonlinearities, and then, an adaptive controller was designed by combining with the backstepping technique. Cite several examples, based on FLS's online approximation capability, a new adaptive fuzzy control scheme was presented in Reference 20. Tong et al citebib21 established a fuzzy state observer and proposed a decentralized adaptive fuzzy optimal control approach for nonlinear large-scale systems. In Reference 22, the fuzzy adaptive output feedback controller was constructed for strict-feedback nonlinear systems, where the problem of virtual control coefficients was well handled by using a convex combination technique. Int J Robust Nonlinear Control. 2020;30:3365-3381. wileyonlinelibrary.com/journal/rnc

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Adaptive Event-Triggered Fault Detection Scheme for Semi-Markovian Jump Systems With Output Quantization

Cited by 203 publications

References 61 publications

Boundary control of stochastic reaction‐diffusion systems with Markovian switching

Boundary control of stochastic reaction‐diffusion systems with Markovian switching

Adaptive event-triggered control for a class of nonlinear systems with periodic disturbances

Asymptotic tracking control for constrained nonstrict‐feedback MIMO nonlinear systems via parameter compensations

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