PID Position Control of Pneumatic Muscle Actuator Based on RBF Neural Network

被引：2

作者：

Liu K. ^{[1
]}

Chen Y. ^{[1
]}

Wu Y. ^{[1
]}

Wang Y. ^{[2
]}

机构：

[1] College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, Jiangsu

[2] College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin, 150040, Heilongjiang

来源：

Huanan Ligong Daxue Xuebao/Journal of South China University of Technology (Natural Science) | 2020年 / 48卷 / 05期

基金：

中国国家自然科学基金;

关键词：

Adaptive PID; Pneumatic artificial muscle; Position control; RBF neural network;

D O I：

10.12141/j.issn.1000-565X.190253

中图分类号：

学科分类号：

摘要：

The static test platform of pneumatic artificial muscle was built, and a series of measurement tests were carried out under pressure of 0.1~0.8 MPa to analyze the characteristics of pneumatic artificial muscle.The mathematical model, which was built based on the theoretical model and test data, shows a high accuracy of solution.In consideration of the influence of external load, gas pressure and system friction on the mathematical model, a PID control strategy based on RBF network was designed with the fast learning ability of RBF network.Under the condition of external load F=50~200 N, the dynamic test platform was built and a number of position control tests were implemented.The results show that the traditional PID control strategy can only achieve better control accuracy within a certain range of external loads, while the proposed strategy is able to adjust the PID parameters adaptively.Moreover, the proposed PID control strategy has the advantages of higher response speed, shorter adjustment time and smaller overshoot, and it can better compensate the mathematical model error and achieve higher control accuracy. © 2020, Editorial Department, Journal of South China University of Technology. All right reserved.

引用

页码：142 / 148

页数：6

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