An Iterative Learning Control Research Based on RBF Neural Network and PSO Algorithm

被引：1
|
作者：
Wang, Shouqin [1 ]
Gong, Yan [1 ]
He, Xingshi [1 ]
机构：
[1] Xian Polytech Univ, Sch Sci, Xian 710048, Peoples R China
来源：
2023 IEEE 12TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS CONFERENCE, DDCLS | 2023年
关键词：
Iterative learning control; RBFNN; Particle swarm optimization algorithm; SYSTEMS; TRACKING; ILC;
D O I：
10.1109/DDCLS58216.2023.10166697
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In order to solve the problem of trajectory shift, a PSO-DRBFNNILC strategy is designed. The first RBFNN is introduced to estimate the output of the ILC system; second RBFNN is built to adaptively adjust the learning gain matrix in the input update law. PSO algorithm is used to find the optimal search step for the update of the weight, center and radius of the activation function. Convergence analysis shows that the estimation error of the weight of the network and the tracking error of the ILC system are bounded. The effectiveness of the control strategy is verified by numerical simulation.
引用
收藏
页码：776 / 781
页数：6
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