Inverse-model-based iterative learning control for unknown MIMO nonlinear system with neural network

This paper provides an inverse-model-based iterative learning control (ILC) for the unknown multi-input multi-output (MIMO) nonlinear system with neural network (NN), where a novel gradient adaptive law is used to update the NN weights both hidden and output layers such a faster convergence can be a...

Full description

Saved in:
Bibliographic Details
Published in:Neurocomputing (Amsterdam) 2023-01, Vol.519, p.187-193
Main Authors: Lv, Yongfeng, Ren, Xuemei, Tian, Jianyan, Zhao, Xiaowei
Format: Article
Language:English
Subjects:
Inversion model
Iterative learning control
Neural network
Nonlinear system
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper provides an inverse-model-based iterative learning control (ILC) for the unknown multi-input multi-output (MIMO) nonlinear system with neural network (NN), where a novel gradient adaptive law is used to update the NN weights both hidden and output layers such a faster convergence can be achieved. First, a three-layer NN structure is introduced to observe the MIMO nonlinear system with input–output data, and a new gradient algorithm is proposed to update the unknown parameters of both hidden and output layers. Then, the input dynamic can be obtained with the NN observer, and the inversion-model-based control is designed. Moreover, the ideal inversion control can be obtained based on the reference signal, and the inverse ILC is designed. The stability of the NN observer and the convergence of the inverse-model-based control are analyzed. Finally, a SCARA manipulator MIMO model is simulated to illustrate the correctness of the proposed methods.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2022.11.040