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Integral BLF-Based Adaptive Neural Constrained Regulation for Switched Systems With Unknown Bounds on Control Gain

Authors :
Tang, Li
He, Kaiyue
Chen, Yang
Liu, Yan-Jun
Tong, Shaocheng
Source :
IEEE Transactions on Neural Networks and Learning Systems; November 2023, Vol. 34 Issue: 11 p8579-8588, 10p
Publication Year :
2023

Abstract

In this article, an integral barrier Lyapunov-function (IBLF)-based adaptive tracking controller is proposed for a class of switched nonlinear systems under the arbitrary switching rule, in which the unknown terms are approximated by radial basis function neural networks (RBFNNs). The IBLF method is used to solve the problem of state constraint. This method constrains states directly and avoids the verification of feasibility conditions. In addition, a completely unknown control gain is considered, which makes it impossible to directly apply previous existing methods. To offset the effect of the unknown control gain, the lower bound of the control gain is added into the barrier Lyapunov function, and a regulating term is introduced into the controller. The proposed control strategy realizes three control objectives: 1) all the signals in the resulting system are bounded; 2) the system output tracks the reference signal to a arbitrarily small compact set; and 3) all the constraint conditions for system states are not violated. Finally, a simulation example is used to show the effectiveness of the proposed method.

Details

Language :
English
ISSN :
2162237x and 21622388
Volume :
34
Issue :
11
Database :
Supplemental Index
Journal :
IEEE Transactions on Neural Networks and Learning Systems
Publication Type :
Periodical
Accession number :
ejs64405055
Full Text :
https://doi.org/10.1109/TNNLS.2022.3151625