A study of self-tuning PID control algorithm for a general nonlinear hysteretic system based on the RBF neural network

To solve the problem that a conventional PID controller working with a general nonlinear hysteretic object is unable to insure the accuracy of a control system,the paper puts forward the method of self-tuning PID controller based on the RBF neural network.The nearest neigbor clustering algorithm is used to train the RBF neural network.The optimal strategy that can regulate the cluster radius automatically is introduced into the guarantee of the cluster rationality.Simulation results show that the controlstrategy can not only have a favorable dynamic tracking performance to the nonlinear hysteretic system,but also possess resistance to disturbance and excellent robustness.