Output feedback control of stochastic nonlinear systems based on extended state observer

Wang, Chunxiao; Zhang, Zhongcai; Wu, Yuqiang

doi:10.1002/acs.3395

Cited by 4 publications

(3 citation statements)

References 33 publications

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“…In the past decades, some achievements have been made in the tracking control problem for stochastic nonlinear systems with adaptive backstepping control methods. [1][2][3][4][5] For instance, under the framework of backstepping technique, the authors in Reference 1 designed a control method to deal with the adaptive fuzzy tracking control problem for stochastic nonlinear systems. In Reference 5, an adaptive neural network tracking control problem with dead zone input was studied for a class of stochastic nonlinear systems.…”

Section: Introductionmentioning

confidence: 99%

Self‐triggered adaptive prescribed‐time tracking control for stochastic nonlinear systems

Zhu

Pan

et al. 2023

Adaptive Control & Signal

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Summary This paper proposes a self‐triggered (ST) adaptive prescribed‐time tracking control method for a class of stochastic nonlinear systems. Different from the existing results, an improved ST mechanism is proposed by adding a judgment condition to reduce the negative effect of excessive design interval on system performance. Based on the one‐to‐one mapping and backstepping technique, an adaptive prescribed‐time tracking control method is proposed, which can make the error converge to the predefined precision set within the predetermined time. Simultaneously, applying the Lyapunov stability method, the boundedness of all signals in the closed‐loop system can be ensured. Finally, a detailed simulation example is provided to show the effectiveness of the proposed control strategy.

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Section: Introductionmentioning

confidence: 99%

Self‐triggered adaptive prescribed‐time tracking control for stochastic nonlinear systems

Zhu

Pan

et al. 2023

Adaptive Control & Signal

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“…As we all know, random noise is one of important factors which maybe influence the stability of NN. Thus, numerous control approaches for stochastic neural networks have been established, for example, event-trigger control [6], output feedback control [7], impulsive control [8][9][10], etc. For the purpose of lowering communication costs, impulsive control has received a lot of attention in control theory.…”

Section: Introductionmentioning

confidence: 99%

Asymptotical Stability and Exponential Stability in Mean Square of Impulsive Stochastic Time-Varying Neural Network

Huang

2023

IEEE Access

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The effect of impulse on stability of neural network is evident not only in performance, that is, impulsive control and impulsive interference. The amount of impulse has a certain impact on stability of neural network. Unlike traditional average impulsive interval, a new strategy is applied in this paper, namely, impulsive density. Based on this strategy, by constructing Lyapunov function, we establish sufficient conditions for mean square asymptotical stability of impulsive stochastic time-varying neural network without time delay. As well as, under this strategy and uniformly asymptotically stable function, by combining trajectory based approach and improved Razumikhin method, mean square exponential stability criterion of impulsive stochastic time-varying neural network with time delay is established. Finally, to demonstrate the viability of our theoretical findings, some instances are provided.

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“…Authors of Reference 12 discussed the stability problem of nonlinear systems with Kalman filter. Output feedback control and application for nonlinear system has been discussed in Reference 13. The method of sliding mode control for nonlinear singular systems has been proposed in Reference 14.…”

Section: Introductionmentioning

confidence: 99%

Finite‐time composite control for fuzzy delayed semi‐Markovian jump systems with multiple disturbances and uncertainty

Zhu

et al. 2023

Adaptive Control & Signal

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Summary This article is concerned with the issue of finite‐time analysis for fuzzy semi‐Markovian jump systems (S‐MJSs) with multiple disturbances. Takagi–Sugeno fuzzy method is applied to address the nonlinear problem of closed‐loop systems. Unlike the existing research, delay, multiple disturbances, generally uncertain transition rate, uncertain parameters are all analyzed in a unify S‐MJSs framework. The aim of this study is to construct a composite control strategy for stable operation of S‐MJSs with multiple disturbances on the basis of the actual system requirements. For S‐MJSs, conservatism of analysis method and uncertainty of actual system are both the significant problems need to be solved urgently. First, the sufficient condition of S‐MJSs is obtained to guarantee the finite‐time stable performance through mathematical analysis and matrix inequality derivation. Second, disturbance observer based control and H∞$$ {H}_{\infty } $$ control are combined to deal with the effects of multiple disturbances which contain incomplete information disturbance and norm bounded disturbance. Third, fuzzy controller is designed to satisfy close‐loop systems finite‐time performance. Finally, the actual nonlinear example is presented to verify the accuracy of the approach.

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Output feedback control of stochastic nonlinear systems based on extended state observer

Cited by 4 publications

References 33 publications

Self‐triggered adaptive prescribed‐time tracking control for stochastic nonlinear systems

Self‐triggered adaptive prescribed‐time tracking control for stochastic nonlinear systems

Asymptotical Stability and Exponential Stability in Mean Square of Impulsive Stochastic Time-Varying Neural Network

Finite‐time composite control for fuzzy delayed semi‐Markovian jump systems with multiple disturbances and uncertainty

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