Trajectory tracking control of a pneumatic X-Y table using neural network based PID control

This paper deals with the use of Neural Network based PID control scheme in order to assure good tracking performance of a pneumatic X-Y table. Pneumatic servo systems have inherent nonlinearities such as compressibility of air and nonlinear frictions present in cylinder. The conventional PID controller is limited in some applications where the affection of nonlinear factor is dominant. In order to track the reference model output, the primary control function is provided by the PID control and then the auxiliary control function is given by neural network for learning and compensating the inherent nonlinearities, A self-excited oscillation method is applied to derive the dynamic design parameters of a linear model. The experiment using the proposed control scheme has been performed and a significant reduction in tracking error is achieved.