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

Le Ma, Qiaoyu Zhang, Tianmiao Wang, Xiaofeng Wu, Jie Liu and Wenjuan Jiang ()
Additional contact information
Le Ma: School of Automation Engineering, Northeast Electric Power University, Jilin 132000, China
Qiaoyu Zhang: School of Automation Engineering, Northeast Electric Power University, Jilin 132000, China
Tianmiao Wang: School of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin 132000, China
Xiaofeng Wu: School of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin 132000, China
Jie Liu: School of Automation Engineering, Northeast Electric Power University, Jilin 132000, China
Wenjuan Jiang: School of Automation Engineering, Northeast Electric Power University, Jilin 132000, China

Mathematics, 2023, vol. 11, issue 13, 1-24

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.

Keywords: mixture basic functions approximating; neural network embedding control; control performance optimization; disturbance observer (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/11/13/2823/pdf (application/pdf)
https://www.mdpi.com/2227-7390/11/13/2823/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:11:y:2023:i:13:p:2823-:d:1177819

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().