Neural-Network-Based Event-Triggered Adaptive Control of Nonaffine Nonlinear Multiagent Systems With Dynamic Uncertainties

Liang, Hongjing; Liu, Guangliang; Zhang, Huaguang; Huang, Tingwen

doi:10.1109/tnnls.2020.3003950

Cited by 383 publications

(131 citation statements)

References 36 publications

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“…On another research forefront, nonlinearity is an intrinsic feature that widely exists in a majority of actual physical systems, which is also an essential element that cannot be neglected in the process of system modeling [20]- [23]. It is recognized that the T-S fuzzy model proposed in [24] is a prevailingly adopted tool for the approximation of nonlinear systems [25]- [29].…”

Section: Introductionmentioning

confidence: 99%

$\mathcal {H}_{\infty }$ Synchronization for Fuzzy Markov Jump Chaotic Systems With Piecewise-Constant Transition Probabilities Subject to PDT Switching Rule

Wang

Xia

Shen

et al. 2021

IEEE Trans. Fuzzy Syst.

254

105

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This paper investigates the non-fragile H∞ synchronization issue for a class of discrete-time T-S fuzzy Markov jump systems. With regard to the T-S fuzzy model, a novel processing method based on matrix transformation is introduced to deal with the double summation inequality containing fuzzy weighting functions, which may be beneficial to obtain conditions with less conservatism. In view of the fact that the uncertainties may occur randomly in the execution of actuator, a non-fragile controller design scheme is presented by virtue of the Bernoulli distributed white sequence. The main novelty of this paper lies in that the transition probabilities of Markov chain are considered to be piecewise time-varying, and whose variation characteristics are described by the persistent dwell-time switching regularity. Then, based on Lyapunov stability theory, it is concluded that the resulting synchronization error system is mean-square exponentially stable with a prescribed H∞ performance in the presence of actuator gain variations. Finally, an illustrative example about Lorenz chaotic systems is provided to show the effectiveness of the established results. Index Terms-T-S fuzzy Markov jump chaotic systems, persistent dwell-time switching, mean-square exponential stability, non-fragile H∞ synchronization.

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Section: Introductionmentioning

confidence: 99%

$\mathcal {H}_{\infty }$ Synchronization for Fuzzy Markov Jump Chaotic Systems With Piecewise-Constant Transition Probabilities Subject to PDT Switching Rule

Wang

Xia

Shen

et al. 2021

IEEE Trans. Fuzzy Syst.

254

105

View full text Add to dashboard Cite

show abstract

“…More recently, in order to meet all kinds of practical needs, the issue of event-triggered control for non-linear systems with uncertainties or external disturbance have been considered by more and more investigators. In [45][46][47][48][49], the event-triggered strategy for uncertain nonlinear systems is proposed by adopting the backstepping technique and adaptive control method. A feedback control scheme with event-triggered mechanism is advanced for uncertain nonlinear systems in [47].…”

Section: Introductionmentioning

confidence: 99%

Adaptive fuzzy control for non‐strict‐feedback stochastic uncertain non‐linear systems based on event‐triggered strategy

Lian

Xia

Sun

et al. 2021

IET Control Theory & Appl

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This paper investigates the issue of adaptive fuzzy control for a category of non-strictfeedback stochastic uncertain non-linear systems. Based on event-triggered mechanism, an adaptive fuzzy controller with a simple form is designed by combining backstepping technique with fuzzy logic system. The devised controller not only ensures the output signal tracking reference signal and all signals of the closed-loop system are bounded in probability but also reduces the communication load between controller and actuator. In addition, the feasibility of the considered event-triggered mechanism is guaranteed by eliminating Zeno behaviour. Finally, the validity of the proposed result is proved by an example. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…A S an important area of control theory, nonlinear system control has been studied for decades, and some excellent control strategies have been proposed, such as input/output linearization [1], backstepping mechanism [2], finite/fixed-time control [3]- [5] and model predictive control [6]. Backstepping method is a promising approach based on recursive design procedure for nonlinear system with strictly feedback forms, see [2], [7], [8] for examples.…”

Section: Introductionmentioning

confidence: 99%

Non-Recursive Control Design for Nonlinear System With Backlash-Like Hysteresis and Disturbance

Gao

Zhao

2021

IEEE Access

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Neural-Network-Based Event-Triggered Adaptive Control of Nonaffine Nonlinear Multiagent Systems With Dynamic Uncertainties

Cited by 383 publications

References 36 publications

$\mathcal {H}_{\infty }$ Synchronization for Fuzzy Markov Jump Chaotic Systems With Piecewise-Constant Transition Probabilities Subject to PDT Switching Rule

$\mathcal {H}_{\infty }$ Synchronization for Fuzzy Markov Jump Chaotic Systems With Piecewise-Constant Transition Probabilities Subject to PDT Switching Rule

Adaptive fuzzy control for non‐strict‐feedback stochastic uncertain non‐linear systems based on event‐triggered strategy

Non-Recursive Control Design for Nonlinear System With Backlash-Like Hysteresis and Disturbance

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