Paper Info
Title
A New Adaptive DS-Based Finite-Time Neural Tracking Control Scheme for Nonstrict-Feedback Nonlinear Systems
Abstract
This article addresses the problem of adaptive finite-time neural tracking control for nonstrict-feedback nonlinear systems via dynamic surface (DS) technique. First, a new quasi-fast finite-time practical stability (QFPS) criterion is proposed for a class of general nonlinear systems. Then, the presented QFPS criterion is applied to design the desired adaptive finite-time neural tracking controller for a class of nonstrict-feedback nonlinear systems. The presented design scheme for the nonstrict-feedback nonlinear system has the following two features: 1) the “explosion of complexity” issue of the backstepping design is addressed by utilizing the DS technique and 2) the structural feature of Gaussian functions is applied to solve the design difficulties caused by the nonstrict-feedback form. It is proved that the designed controller for the nonstrict-feedback nonlinear system can make the resulting closed-loop system stabilizable in a quasi-fast finite time and the tracking error converges to a sufficiently small neighborhood of the origin. Finally, the simulation results are given to show the validity and practicability of the proposed design scheme.
Year
DOI
Venue
2022
10.1109/TSMC.2020.3009405
IEEE Transactions on Systems, Man, and Cybernetics: Systems
Keywords
DocType
Volume
Adaptive control,dynamic surface (DS) technique,neural networks,nonstrict-feedback nonlinear systems,quasi-fast finite-time control
Journal
52
Issue
ISSN
Citations 
2
2168-2216
0
PageRank 
References 
Authors
0.34
15
4
Name
Order
Citations
PageRank
Dong-Yang Jin100.34
Ben Niu247829.91
Huan-Qing Wang300.34
Dong Yang411618.09