Adaptive Neural Network Dynamic Surface Control Algorithm for Pneumatic Servo System

Abstract

Pneumatic servo system is widely applied in many industries, which has advantages comparing with electromechanical and hydraulic system because of its fast response, high-performance quality and low-cost. However, the servo control methods for pneumatic system still have some inevitable drawbacks and problems remaining to be researched. In this paper, a position feedback dynamic surface control is designed which is based on our pneumatic actuator model. More importantly, in order to overcome model uncertainties, noise interference and external force disturbance, an adaptive neural network dynamic surface controller is proposed to overcome the negative effects. Besides, the stability of the pneumatic system is substantiated by Lyapunov stability theorem. Finally, the results of simulation experiment also prove that the adaptive neural network dynamic surface controller has more advantages than the traditional controllers in pneumatic position servo control.