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

被引：1
|
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
收藏
页码：142 / 148
页数：6
相关论文
共 18 条
  • [1] VERRELST B, HAM R V, VANDERBORGHT B, Et al., Second generation pleated pneumatic artificial muscle and its robotic applications [J], Advanced Robotics, 20, 7, pp. 783-805, (2006)
  • [2] TENG Yan, YANG Gang, LI Xiaoning, Et al., Application of rehabilitation robotics technology and pneumatic muscle for injured lower limb [J], Machine Tool & Hydraulics, 40, 15, pp. 137-140, (2010)
  • [3] LIU Yanbin, LI Zhisong, DI Fuyan, The model and characteristics research of variable stiffness parallel vibration system based on pneumatic artificial muscle [J], Chinese Hydraulics & Pneumatics, 11, pp. 52-56, (2013)
  • [4] SHI Guanglin, ZHOU Aiguo, ZHONG Tingxiu, Nolinear characteristics of the multi2dof platform driven by pneumatic artificial musle [J], Journal of South China University of Technology(Natural Science Edition), 32, 9, pp. 41-45, (2004)
  • [5] GEORGE Andrikopoulos, GEORGE Nikolakopoulos, IOANNIS Arvanitakis, Et al., Piecewise affine modeling and constrained optimal control for a pneumatic artificial muscle [J], IEEE Transactions on Industrial Electronics, 61, 2, pp. 904-914, (2014)
  • [6] YU Haitao, GUO Wei, TAN Hongwei, Et al., Design and control on antagonistic bionic joint driven by pneumatic muscles actuators, Journal of Mechanical Engineering, 48, 17, pp. 1-9, (2012)
  • [7] CASTANEDA L A, LUVIANO-JUAREZ A, CHAIREZ I., Robust trajectory tracking of a delta robot through adaptive active disturbance rejection control [J], IEEE Transactions on Control Systems Technology, 23, 4, pp. 1387-1398, (2015)
  • [8] ZHANG W, YE X, JI X., RBF neural network adaptive control for space robots without speed feedback signal [J], Transactions of the Japan Society for Aeronautical & Space Sciences, 56, 6, pp. 317-322, (2013)
  • [9] WANG Jian, JIANG Xianzhi, Joint modeling and position fuzzy control of rehabilitation robot driven by pneumatic muscle, Machinery, 49, 559, pp. 10-13, (2011)
  • [10] SHEN Wei, SHI Guanglin, Adaptive fuzzy CAMC position tracking control based on pneumatic artificial muscle [J], Journal of Shanghai Jiaotong University(Natural Science Edition), 46, 4, pp. 579-583, (2012)
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