Neural networks‐based adaptive finite‐time prescribed performance fault‐tolerant control of switched nonlinear systems

Wang, Xinjun; Niu, Ben; Zhao, Ping; Song, X.

doi:10.1002/acs.3210

Cited by 20 publications

(20 citation statements)

References 60 publications

(93 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On another frontier, the definition of finite-time stability (FTS) characterizes the trajectories of the system within a fixed-time interval and has grabbed enormous attention from researchers. [20][21][22] In contrast to asymptotic Lyapunov stability, FTS implies that system states must be kept within certain limits in the finite-time interval. From a practical point of view, the dynamic behavior of the systems must not exceed specified limits during a fixed time interval.…”

Section: Introductionmentioning

confidence: 99%

Finite‐time resilient control for networked control systems with multiple cyber‐attacks: Memory/adaptive event‐triggered scheme

Sathishkumar

Liu

2022

Adaptive Control & Signal

View full text Add to dashboard Cite

This article addresses the finite-time memory and finite-time adaptive event-triggered control issues for networked control systems (NCSs) with randomly occurring cyber-attacks. A comprehensive stochastic attack model is developed that includes false-data injection attacks and denial-of-service attacks. In our first problem, the purpose of the memory event-triggered scheme is used to release certain packets to establish new events contrasting with the existing event-triggered method. In this fashion, the event generator can make a more accurate decision and better control performance can be expected. On the other hand, our second problem introduces an adaptive event-triggered scheme to reduce unnecessary communication transmissions in a network with an adaptive varying-threshold while maintaining acceptable system performance. In both problems where malicious signals are injected by adversaries, a random variable is presented to relate cyber-attacks. Using Lyapunov stability theory, integral inequality and linear matrix inequality, a sufficient condition is constructed for both problems to achieve the exponentially mean-square finite-time boundedness for NCSs under stochastic attacks. Finally, three numerical examples are simulated to demonstrate the effectiveness of the proposed methods.

show abstract

Section: Introductionmentioning

confidence: 99%

Finite‐time resilient control for networked control systems with multiple cyber‐attacks: Memory/adaptive event‐triggered scheme

Sathishkumar

Liu

2022

Adaptive Control & Signal

View full text Add to dashboard Cite

show abstract

“…In the real world, almost all actual systems have nonlinear characteristics. Many nonlinear system control problems have attracted the attention of researchers 1‐3 . With the development of science and technology, the complexity of nonlinear systems is getting higher and higher.…”

Section: Introductionmentioning

confidence: 99%

“…Many nonlinear system control problems have attracted the attention of researchers. [1][2][3] With the development of science and technology, the complexity of nonlinear systems is getting higher and higher. In many studies, backstepping has become a very popular control strategy for nonlinear systems.…”

Section: Introductionmentioning

confidence: 99%

Adaptive output‐feedback control for a class of nonlinear systems based on optimized backstepping technique

Shen

Wang

et al. 2022

Adaptive Control & Signal

Self Cite

View full text Add to dashboard Cite

In this article, an adaptive output-feedback control for a class of strict-feedback nonlinear systems is developed based on optimized backstepping technique.Neural networks are utilized to approximate unknown functions, while a state observer is designed to estimate the unmeasurable system state signals. Since the presented optimized control scheme requires training the adaptive parameters for reinforcement learning (RL), it will be more challenging for designing control algorithms and deriving the adaptive update rates. In general, optimization control is designed based on the solution of Hamilton-Jacobi-Bellman equation, but solving the equation is very difficult due to the inherent nonlinearity and intractability. So, RL strategy of actor-critic architecture is used. According to the Lyapunov stability theory, it is proved that all signals of the closed-loop systems are semi-global uniformly ultimately bounded. Finally, the results of the simulation cases are provided to show the effectiveness of the designed controller scheme.

show abstract

“…In the past few decades, the problems of adaptive control have attracted widespread attention in the control community. [1][2][3][4] Stochastic disturbances seriously affect the performance of nonlinear systems in engineering applications. Due to the existence of stochastic disturbances, the controller design of stochastic nonlinear systems is more complex than for deterministic systems.…”

Section: Introductionmentioning

confidence: 99%

“…Then the Nussbum function is used to stabilize the system. The observer states x1 and x2 are shown in Figs 4.…”

mentioning

confidence: 99%

Adaptive neural output‐feedback tracking control for a class of stochastic nonlinear systems with output constraint and unknown control coefficients

Shen

Wang

Yin

2021

Intl J Robust & Nonlinear

Self Cite

View full text Add to dashboard Cite

This article addresses adaptive neural output-feedback tracking control problem for a class of stochastic nonlinear systems with output constraint and unknown control coefficients. A state observer is designed to estimate the unmeasurable system states. Nussbaum gain technique is employed to deal with the problem of unknown control coefficients. Simultaneously, aiming at the output constraint requirements of stochastic systems, the tan-type barrier Lyapunov function (BLF) structure is proposed. The constraint requirement on the system output trace error is not violated during the operation in the sense of probability. An adaptive output-feedback controller is designed to ensure that the output tracking error converges to a small region of the origin. The control scheme ensures that all signals in the closed-loop systems are semi-global uniformly ultimately bounded. Results of simulation are presented to prove the effectiveness of the theoretical analysis.

show abstract

Neural networks‐based adaptive finite‐time prescribed performance fault‐tolerant control of switched nonlinear systems

Cited by 20 publications

References 60 publications

Finite‐time resilient control for networked control systems with multiple cyber‐attacks: Memory/adaptive event‐triggered scheme

Finite‐time resilient control for networked control systems with multiple cyber‐attacks: Memory/adaptive event‐triggered scheme

Adaptive output‐feedback control for a class of nonlinear systems based on optimized backstepping technique

Adaptive neural output‐feedback tracking control for a class of stochastic nonlinear systems with output constraint and unknown control coefficients

Contact Info

Product

Resources

About