Decentralized Finite-Time Adaptive Neural Output-Feedback Quantized Control for Switched Nonlinear Large-Scale Delayed Systems

IF 3.2 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

International Journal of Robust and Nonlinear Control Pub Date : 2024-12-06 DOI:10.1002/rnc.7765

Zhenhua Li, Hongtian Chen, Wentao Wu, Zehua Jia, Weidong Zhang

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引用次数: 0

Abstract

This paper considers the problem of decentralized finite-time adaptive neural output-feedback quantized control for a class of switched nonlinear large-scale delayed systems. A switched high-gain quantized state observer is therefore constructed for each subsystem to estimate unavailable system states. Different from the traditional Lyapunov–Krasovskii functional method, multiple Lyapunov–Krasovskii functions are introduced to develop the decentralized adaptive output-feedback control strategy with neural network approximation for the switched nonlinear large-scale delayed systems. Under a category of switching signals with persistent dwell time, all signals in the closed-loop switched system are semi-globally uniformly ultimate bounded. Meanwhile, the tracking errors can remain in a small domain of origin in finite time. Case studies are finally used to illustrate the flexibility and effectiveness of the proposed control approach, including the switched two continuous stirred tank reactor delayed systems.

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来源期刊

International Journal of Robust and Nonlinear Control 工程技术-工程：电子与电气

CiteScore

6.70

自引率

20.50%

发文量

505

审稿时长

2.7 months

期刊介绍： Papers that do not include an element of robust or nonlinear control and estimation theory will not be considered by the journal, and all papers will be expected to include significant novel content. The focus of the journal is on model based control design approaches rather than heuristic or rule based methods. Papers on neural networks will have to be of exceptional novelty to be considered for the journal.