Adaptive neural network asymptotic tracking control for nonstrict feedback stochastic nonlinear systems

Liu, Yongchao; Zhu, Qidan

doi:10.1016/j.neunet.2021.06.011

Cited by 18 publications

(5 citation statements)

References 58 publications

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“…In the section, the FTSY of stochastic MLNs is discussed by using FTST theorem of deterministic systems. Theorem 1: Under H1-H2 and the controller (11), the stochastic error system (3) is quasi-stable under finite-time pth moment…”

Section: Resultsmentioning

confidence: 99%

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Synchronization of Stochastic Multilayer Networks by Finite-Time Controller

Gao

Zhang

et al. 2023

IEEE Access

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It is well known that complex networks are inevitably affected by random disturbances. In the study of complex network stability, finite-time control has always been a research hot spot. This paper discuss finite-time synchronization (FTSY) of stochastic multilayer networks (MLNs). First, novel finite-time stability (FTST) theorems of deterministic complex dynamical system are given, and the expression of finite stability time is also estimated. Second, sufficient criteria are obtained for FTSY of stochastic MLNs. Compared with some the existing research results of FTSY in stochastic MLNs, FTSY of stochastic MLNs is realized by using FTST of deterministic complex dynamical systems, and the relationship between control intensity and network layers is also discussed under the condition of minimum convergence time, which will be meaningful to help how to select appropriate in actual complex system control. Lastly, the availability of the method is checked by numerical simulation base on the theory proposed in this paper.INDEX TERMS Multilayer networks, finite-time control, synchronization.

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Section: Resultsmentioning

confidence: 99%

“…In [10], Wu synchronization of stochastic network by periodically intermittent discrete observation control. In [11], Liu discussed stochastic nonlinear systems by tracking feedback control. However, these research results were asymptotically stable systems in infinite time domain.…”

mentioning

confidence: 99%

Synchronization of Stochastic Multilayer Networks by Finite-Time Controller

Gao

Zhang

et al. 2023

IEEE Access

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“…Remark The topological structure of MTN is shown in Figure 1. As a network structure similar to radial basis function neural network (RBFNN), 9 MTN is composed of three layers: input layer, middle layer and output layer. The major difference between the MTN and RBFNN is the way of processing information of the middle layer.…”

Section: Preliminary Preparation Of Problemsmentioning

confidence: 99%

“…5 Specifically, adaptive control has become an effective method to solve the control problems of stochastic nonlinear systems. Meanwhile, by combining the approximation-based intelligent control method with the traditional adaptive backstepping method, many meaningful research results have been achieved, such as neural network (NN) control, [6][7][8][9] fuzzy control, 10,11 and multi-dimensional Taylor network (MTN) control. 12,13 Specially, since MTN has the advantages of simple structure, small computational effort and fast function approximation, 14 MTN-based control method has gained more and more attention and been successfully applied to different types of stochastic nonlinear systems, such as stochastic nonlinear systems with input constraints, [15][16][17] stochastic nonlinear systems with multiple faults, 13 and large-scale stochastic nonlinear systems.…”

Section: Introductionmentioning

confidence: 99%

Event‐triggered adaptive tracking control for stochastic nonlinear systems under predetermined finite‐time performance

Wang,

Zhu,

et al. 2024

Adaptive Control & Signal

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SummaryIn this paper, an event‐triggered adaptive tracking control strategy is proposed for strict‐feedback stochastic nonlinear systems with predetermined finite‐time performance. Firstly, a finite‐time performance function (FTPF) is introduced to describe the predetermined tracking performance. With the help of the error transformation technique, the original constrained tracking error is transformed into an equivalent unconstrained variable. Then, the unknown nonlinear functions are approximated by using the multi‐dimensional Taylor networks (MTNs) in the backstepping design process. Meanwhile, an event‐triggered mechanism with a relative threshold is introduced to reduce the communication burden between actuators and controllers. Furthermore, the proposed control strategy can ensure that all signals of the closed‐loop system are bounded in probability and the tracking error is within a predefined range in a finite time. In the end, the effectiveness of the proposed control strategy is verified by two simulation examples.

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“…The nonstrict feedback nonlinear systems represent broader form than the strict feedback nonlinear systems. Many industrial physical nonlinear systems are with nonstrict feedback form, such as electromechanical systems and chemical systems [1][2][3]. An adaptive neural network (NN) event-triggered consensus control problem was proposed for nonstrict feedback nonlinear systems [4].…”

Section: Introductionmentioning

confidence: 99%

Finite‐time command filter‐based adaptive tracking control for nonstrict feedback nonlinear systems with full‐state restrictions and unmodeled dynamics

Zhu

Huang

Ding

et al. 2023

Asian Journal of Control

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The finite-time command filter tracking control for a class of nonstrictly feedback nonlinear systems with unmodeled dynamics and full-state constraints is investigated in this paper. The hyperbolic tangent function is used as a nonlinear mapping technique to solve the obstacle of the full-state constraints. A new adaptive finite time control method is proposed through command filtering reverse engineering, and the shortcomings of the dynamic surface control (DSC) method are overcome by the error compensation mechanism. Dynamic signal is designed to handle dynamical uncertain terms. Normalization signal is designed to handle input unmodeled dynamics. Unknown nonlinear functions are approximated by radial basis function neural networks. Based on the Lyapunov stability theory, it is proved that all signals in the closed-loop system are semi-globally consistent and finally bounded and the output tracking error converges in finite time. Two numerical examples are utilized to verify the effectiveness of the proposed control approach.

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Adaptive neural network asymptotic tracking control for nonstrict feedback stochastic nonlinear systems

Cited by 18 publications

References 58 publications

Synchronization of Stochastic Multilayer Networks by Finite-Time Controller

Synchronization of Stochastic Multilayer Networks by Finite-Time Controller

Event‐triggered adaptive tracking control for stochastic nonlinear systems under predetermined finite‐time performance

Finite‐time command filter‐based adaptive tracking control for nonstrict feedback nonlinear systems with full‐state restrictions and unmodeled dynamics

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