Sliding Mode Control Algorithm Based on RBF Neural Network Observer for Pneumatic Position Servo System

Pneumatic actuators gain much popularity in many industries where there is great demand for a safety working environment and dynamic performance of a system. But the nonlinear characteristics such as friction and air compressibility add to difficulty of controlling so that constrain its wider application. In this paper, in order to overcome the disadvantage like the inaccuracy of parameters, uncertainty of the model and disturbance, a sliding mode observer with RBF neural network is proposed. The RBF neural network is designed to appropriate the nonlinear parts of the model, and the robustness of sliding mode control can guarantee the stability of control system under perturbation and model uncertainty. The stability of this algorithm is proved by Lyapunov theory. Finally, simulations done with Simulink is designed to examine the effectiveness of our algorithm. The result shows this algorithm has good performance.