A neural network fractional order PID controller for FOLPD process

Abstract

Fractional order PID, which involve integration and differentiation of non-integer order, is increasingly being used in the fields of control system, robotics, signal processing and circuit theory. Based on neural network, this paper introduce a new approach for design fractional order PID controller. A self-learning PID controller with five dimension parameters is realized by using parameter turning strategy of RBF neural network. Further apply the fractional order PID controller to a typical FOLPD process like pulp consistency control system. In particular, pulp consistency system is a typical FOLPD process. The simulation results show that this kind of controller has a better effect for pulp consistency system than traditional PID and PIDNN controller.