Fixed-time adaptive neural tracking control for a class of uncertain nonstrict nonlinear systems

This paper addresses the fixed-time adaptive neural control of nonstrict feedback nonlinear system. With the help of neural networks and the backstepping technical, a fixed-time adaptive neural control scheme is presented. To guarantee closed-loop stability, a new semiglobal practical fixed-time sta...

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Bibliographic Details
Published inNeurocomputing (Amsterdam) Vol. 363; pp. 273 - 280
Main Authors Ba, Desheng, Li, Yuan-Xin, Tong, Shaocheng
Format Journal Article
LanguageEnglish
Published Elsevier B.V 21.10.2019
Subjects
Adaptive control
Backstepping
Fixed-time control
Neural networks
Nonstrict feedback nonlinear systems
Backstepping
Fixed-time control
Nonstrict feedback nonlinear systems
Neural networks
Adaptive control
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Summary:This paper addresses the fixed-time adaptive neural control of nonstrict feedback nonlinear system. With the help of neural networks and the backstepping technical, a fixed-time adaptive neural control scheme is presented. To guarantee closed-loop stability, a new semiglobal practical fixed-time stability (SPFTS) criterion is set up. Based on the established SPFTS criterion, we can show that both the tracking performance and the closed-loop stability can be preserved in a fixed time via the presented approach. Compared with the existing finite-time control, the convergence time of the propose fixed-time control scheme does not rely on the initial states. Finally, the proposed technique is demonstrated with simulation results.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2019.06.063