Self-learning fuzzy PID controller based on neural networks

For conventional PID tuner and fuzzy inference systems based on expertise problem will arise when expertise of the process is not enough. Artificial neural networks have self-learning capability, however, the change of their weights can not be understood, This paper describes the structures of self-learning neuro-fuzzy networks and shrinking-span membership functions, and presents a neuro-fuzzy PID (NFPID) controller. The NFPID controller has the capability of self-extracting inference rules, and its parameters have explicitly physical definitions. By using the RBF neural network inverse model, a hybrid learning procedure was put forward. Various simulation results demonstrated that the NFPID controller described has very good performances