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Mixture Basis Function Approximation and Neural Network Embedding Control for Nonlinear Uncertain Systems with Disturbances

Authors :
Le Ma
Qiaoyu Zhang
Tianmiao Wang
Xiaofeng Wu
Jie Liu
Wenjuan Jiang
Source :
Mathematics, Vol 11, Iss 13, p 2823 (2023)
Publication Year :
2023
Publisher :
MDPI AG, 2023.

Abstract

A neural network embedding learning control scheme is proposed in this paper, which addresses the performance optimization problem of a class of nonlinear system with unknown dynamics and disturbance by combining with a novel nonlinear function approximator and an improved disturbance observer (DOB). We investigated a mixture basic function (MBF) to approximate the unknown nonlinear dynamics of the system, which allows an approximation in a global scope, replacing the traditional radial basis function (RBF) neural networks technique that only works locally and could be invalid beyond some scope. The classical disturbance observer is improved, and some constraint conditions thus are no longer needed. A neural network embedding learning control scheme is exploited. An arbitrary type of neural network can be embedded into a base controller, and the new controller is capable of optimizing the control performance by tuning the parameters of neural network and satisfying the Lyapunov stability simultaneously. Simulation results verify the effectiveness and advantage of our proposed methods.

Details

Language :
English
ISSN :
22277390
Volume :
11
Issue :
13
Database :
Directory of Open Access Journals
Journal :
Mathematics
Publication Type :
Academic Journal
Accession number :
edsdoj.06941671f19043879845db6182d7a1b0
Document Type :
article
Full Text :
https://doi.org/10.3390/math11132823