Sliding mode control of singular stochastic Markov jump systems

Feng, Zhiguang; Li, Jiangrong; Shi, Peng; Du, Haiping; Jiang, Zhengyi

doi:10.1016/b978-0-12-823739-7.00014-8

Cited by 18 publications

(30 citation statements)

References 16 publications

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“…In the future, we will investigate how to extend the adaptive fixed-time control method for the nonlinear system with time delay and event-triggered method. [43][44][45][46][47][48][49]…”

Section: Discussionmentioning

confidence: 99%

Adaptive fuzzy dual‐performance fault‐tolerant control for interconnected nonlinear systems

HaoSiwen

Xuehong

PanYingnan

2021

Adaptive Control & Signal

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This article focuses on the decentralized adaptive fuzzy fixed-time fault-tolerant control issue for the error-constrained interconnected nonlinear systems with unknown actuator faults possessing dead zone. The unknown nonlinear functions can be modeled via fuzzy logic systems. By utilizing the parameter estimation method, the effect of unknown actuator faults possessing dead zone can be compensated. To guarantee the predefined dynamic performance of state tracking errors, the barrier Lyapunov functions and prescribed performance functions are introduced. Then, a dual-performance fault-tolerant control method that can guarantee fast transient performance and predefined performance of state tracking errors is proposed via using the decentralized backstepping technique. In addition, on the basis of the Lyapunov stability theory and the fixed-time criterion, it is proved that the predefined performance of full-state errors and the stability of closed-loop systems can be guaranteed. Finally, two numerical examples are provided to illustrate the effectiveness of the proposed control scheme.

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“…In the future, we will investigate how to extend the adaptive fixed-time control method for the nonlinear system with time delay and event-triggered method. [43][44][45][46][47][48][49]…”

Section: Discussionmentioning

confidence: 99%

Adaptive fuzzy dual‐performance fault‐tolerant control for interconnected nonlinear systems

HaoSiwen

Xuehong

PanYingnan

2021

Adaptive Control & Signal

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“…This is really convenient for the application of SMC, but this method lead to the control input chattering [9]. Besides, when utilizing SMC, the input matrix is assumed to be constant, which means that, B has no uncertainty always [10]. This assumption is also unreasonable since the system matrix A has uncertainty ∆A, the input matrix B should also has uncertainty ∆B.…”

Section: Introductionmentioning

confidence: 99%

Sliding Mode Based Robust Learning Control for Uncertain Linear System With Mismatched Disturbance

Chen

Liu

et al. 2021

IEEE Access

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A robust learning control strategy is presented for uncertain linear system with mismatched disturbance. A linear model with both mismatched uncertainty and disturbance is proposed firstly, in which the information of uncertainties and disturbance are unknown. Based on a reference model, a tracking error based linear tracking model is built. What's more, the input matrix of the linear model is assumed to be suffering from uncertainty. An extended state observer is built to approximate the uncertainty and disturbance online, and then a novel state tracking error based sliding surface is proposed. The stability of the sliding surface is guarantee by the designing parameters. A sliding mode based learning controller is designed for the closed-loop whole uncertain system. Moreover, a numerical simulation result on hypersonic flight vehicles is proposed to test the presented method.

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“…Over the past decades, the research on stochastic nonlinear systems has attracted significant attention because of the theoretical and practical challenges [1][2][3][4]. In [2], a disturbance observer-based method was proposed for stochastic systems.…”

Section: Introductionmentioning

confidence: 99%

“…In [2], a disturbance observer-based method was proposed for stochastic systems. In [3], the sliding mode control method was proposed for singular stochastic Markov systems. In [4], state-feedback control schemes were presented for high-order stochastic nonlinear systems.…”

Section: Introductionmentioning

confidence: 99%

“…In [4], state-feedback control schemes were presented for high-order stochastic nonlinear systems. The above control methods [1][2][3][4] require that the dynamic functions are totally known or linearly parameterized. To relax the restriction of the linear parametric, neural network (NN) or fuzzy logic systems have been extensively used to cope with tracking control problem for uncertain stochastic nonlinear systems, and a series of useful solutions have been obtained [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

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Neural network adaptive finite‐time control of stochastic nonlinear systems with full state constraints

Zhu

Liu

2020

Asian Journal of Control

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This paper investigates the issue of neural network adaptive finite-time tracking control for stochastic nonlinear systems subject to full state constraints. In the controller design process, neural networks are employed to cope with the packed uncertainties and the log-type barrier Lyapunov functions are introduced to prevent the violation of the state constraints. By using approximated-based neural networks and adaptive backstepping technique, a novel finite-time control approach is presented. The designed control method not only makes the tracking error converge to a small neighborhood of the origin in a finite time, but also surmounts the effect of state constraints to system performance. A numerical simulation example is provided to illustrate the validity of the designed control method.

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Sliding mode control of singular stochastic Markov jump systems

Cited by 18 publications

References 16 publications

Adaptive fuzzy dual‐performance fault‐tolerant control for interconnected nonlinear systems

Adaptive fuzzy dual‐performance fault‐tolerant control for interconnected nonlinear systems

Sliding Mode Based Robust Learning Control for Uncertain Linear System With Mismatched Disturbance

Neural network adaptive finite‐time control of stochastic nonlinear systems with full state constraints

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