Observer-based adaptive event-triggered tracking control for nonlinear MIMO systems based on neural networks technique

In this paper, the issue of adaptive neural networks event-triggered control for nonstrict-feedback nonlinear multi-input-multi-output(MIMO) systems containing unmeasured states is investigated. All unmeasured states are approximated by using neural networks observer. Meanwhile, the neural networks...

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Bibliographic Details
Published in:Neurocomputing (Amsterdam) 2021-04, Vol.433, p.71-82
Main Authors: Wang, Sanxia, Xia, Jianwei, Sun, Wei, Shen, Hao, Zhang, Huasheng
Format: Article
Language:English
Subjects:
Adaptive control
Event-triggered control
Multi-input-multi-output nonlinear systems
Neural networks observer
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Summary:In this paper, the issue of adaptive neural networks event-triggered control for nonstrict-feedback nonlinear multi-input-multi-output(MIMO) systems containing unmeasured states is investigated. All unmeasured states are approximated by using neural networks observer. Meanwhile, the neural networks are used to estimate the unknown continuous function at each step of recursion. Then, an observer-based adaptive neural networks event-triggered tracking control strategy is proposed based on backstepping technique. The designed controller enables the outputs of the system to track the target trajectory within a small bounded error range, and all signals in the closed-loop system are bounded.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2020.12.050