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Neural Computing and Applications

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17-08-2021 | Original Article

Adaptive NN prescribed performance control design for uncertain switching nonlinear systems with periodically time-varying parameters

Authors: Xiaoli Yang, Jing Li, Shuiyan Wu, Xiaobo Li

Published in: Neural Computing and Applications | Issue 1/2022

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Abstract

This paper addresses a tracking control problem with prespecified accuracy for uncertain switching nonlinear systems under arbitrary switching with periodically time-varying parameters. To approximate the unknown nonlinear functions and unknown periodically time-varying parameters, the radial basis function neural network and Fourier series expansion are introduced, respectively. Compared with the previous results of unknown nonlinear switching system approximation, the upper bounds of the approximation errors are considered for the first time. And then, a new adaptive NN control scheme is constructed by using backstepping technique and common Lyapunov function theory. It can be proved that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded and the tracking error converges to the prescribed neighborhood of zero. Two examples are provided to verify the feasibility and advantages of the proposed approach in this paper.

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Title: Adaptive NN prescribed performance control design for uncertain switching nonlinear systems with periodically time-varying parameters
Authors: Xiaoli Yang
Jing Li
Shuiyan Wu
Xiaobo Li
Publication date: 17-08-2021
Publisher: Springer London
Published in: Neural Computing and Applications / Issue 1/2022
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-021-06387-8

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