Journal of South China University of Technology (Natural Science Edition) ›› 2020, Vol. 48 ›› Issue (5): 142-148.doi: 10.12141/j.issn.1000-565X.190253

• Mechanical Engineering • Previous Articles    

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

LIU Kai1 CHEN Yining1 WU Yang1 WANG Yangwei2   

  1. 1． College of Mechanical and Electrical Engineering，Nanjing University of Aeronautics and Astronautics，Nanjing 210016，Jiangsu，China; 2． College of Mechanical and Electrical Engineering，Northeast Forestry University，Harbin 150040，Heilongjiang，China
  • Received:2019-05-12 Revised:2019-12-23 Online:2020-05-25 Published:2020-05-01
  • Contact: 刘凯(1981-)，男，博士，副教授，主要从事仿生机器人、数控技术研究。 E-mail:liukai@nuaa.edu.cn
  • About author:刘凯(1981-)，男，博士，副教授，主要从事仿生机器人、数控技术研究。
  • Supported by:
    Supported by the National Natural Science Foundation of China (51405229) and the Natural Science Founda-tion of Jiangsu Province (BK20151470，BK20171416)

PDF

127

Abstract: 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 solu-tion． In consideration of the influence of external load，gas pressure and system friction on the mathematical mod-el，a PID control strategy based on ＲBF network was designed with the fast learning ability of ＲBF network． Un-der 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 pa-rameters 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 a-chieve higher control accuracy．

Key words: pneumatic artificial muscle, RBF neural network, adaptive PID, position control

CLC Number: