Remodeling of fuzzy PID controller with neural network

Aimed at computational complexity and poor real-time performance in fuzzy PID control algorithm, selecting fuzzy PID controller as study object, an equivalent RBF neural network model with universal function approximating ability was utilized to accurately approach a known fuzzy PID controller. After that, the same plant model which was controlled by the fuzzy PID controller and the equivalent RBF NN model was simulated with different reference inputs, respectively. Simulated results show that control qualities from two different controllers were extremely similar. Therefore, the fuzzy PID controller can be replaced by an equivalent RBF NN model in order to reduce the computational complexity, avoid the dimensional curse and improve the real-time performance.