title: BP neural network tuned PID controller for position tracking of a pneumatic artificial muscle.
abstract: BACKGROUND
  Although Pneumatic Artificial Muscle (PAM) has a promising future in rehabilitation robots, it's difficult to realize accurate position control due to its highly nonlinear properties.
  
  
  OBJECTIVE
  This paper deals with position control of PAM.
  
  
  METHODS
  To describe the hysteresis inside PAM, a polynomial based phenomenological function is developed. Based on the phenomenological model for PAM and analysis of pressure dynamics within PAM, an adaptive cascade controller is proposed. Both outer loop and inner loop employ BP Neural Network tuned PID algorithm. The outer loop is to handle high nonlinearities and unmodeled dynamics of PAM, while the inner loop is responsible for nonlinearities caused by pressure dynamics.
  
  
  RESULTS
  Experimental results show high tracking accuracy as compared with a convention PID controller.
  
  
  CONCLUSIONS
  The proposed controller is effective in improving performance of PAM and will be implemented in a rehabilitation robot.
authors.forename: Jizhuang
authors.initials: J
authors.lastname: Fan
authors.forename: Jun
authors.initials: J
authors.lastname: Zhong
authors.forename: Jie
authors.initials: J
authors.lastname: Zhao
authors.forename: Yanhe
authors.initials: Y
authors.lastname: Zhu
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D006801:Humans; D008962:Models, Theoretical; D009132:Muscles; D016571:Neural Networks, Computer; D019736:Prostheses and Implants; D012371:Robotics
keywords: BP neural network tuned PID; Mckibben muscles; cascade position controller; empirical modeling