Multi-PID controller parameters optimization of electro hydraulic servo system

被引：1

作者：

Feng H. ^{[1
]}

Jiang J.-Y. ^{[2
]}

Song Q.-Y. ^{[1
]}

Ma W. ^{[3
]}

Yin C.-B. ^{[3
]}

Cao D.-H. ^{[4
]}

机构：

[1] School of Artificial Intelligence, Nanjing University of Information Science & Technology, Jiangsu, Nanjing

[2] School of Computer Science, Nanjing University of Information Science & Technology, Jiangsu, Nanjing

[3] United Institute of Excavator Key Technology, Nanjing Tech University, Jiangsu, Nanjing

[4] SANY Group Co., Ltd., Jiangsu, Kunshan

来源：

Kongzhi Lilun Yu Yingyong/Control Theory and Applications | 2024年 / 41卷 / 04期

基金：

中国国家自然科学基金;

关键词：

electro hydraulic servo system; intelligent control; PSO; robot; trajectory control;

D O I：

10.7641/CTA.2023.20271

中图分类号：

学科分类号：

摘要：

In order to solve the problem of parameters optimization of multi-proportional-integral-derivative (PID) controllers in different electro-hydraulic servo systems of boom, stick and bucket for robotic excavator, and improve the tracking accuracy of bucket tooth tip, the parameters of PID controllers are optimized by an improved particle swarm optimization algorithm (PSO). Firstly, the mathematical mechanism model of electro-hydraulic servo system is established. Based on the theoretical model, the transfer function is obtained by recursive least square identification method (RLS). Secondly, an improved PSO algorithm is proposed, which adopts nonlinear adaptive inertia weight, introduces asynchronous change strategy, designs elite mutation method. Then, a simulation verification platform is build to track the step and sinusoidal trajectory, and compare the differences between the traditional Z-N method, the basic PSO algorithm and the improved PSO algorithm. Finally, taking the leveling and slope repair as the representative working condition, the experimental verification is carried out based on a 23 ton robotic excavator experimental platform. The experimental results show that the improved PSO algorithm has the highest tracking accuracy, and significantly improves the trajectory tracking accuracy compared with the basic PSO algorithm. © 2024 South China University of Technology. All rights reserved.

引用

页码：763 / 767

页数：4

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