| Title |
Design of a Neural Network Based Self-Tuning Fuzzy PID Controller |
| Authors |
임정흠(Im, Jeong-Heum) ; 이창구(Lee, Chang-Goo) |
| Keywords |
Fuzzy ; Neural network ; PID(Proportional Integral Derivative) ; Nonlinear control ; Magnetic levitation |
| Abstract |
This paper describes a neural network based fuzzy PID control scheme. The PID controller is being widely used in industrial applications. However, it is difficult to determine the appropriated PID gains in nonlinear systems and systems with long time delay and so on. In this paper, we re-analyzed the fuzzy controller as conventional PID controller structure, and proposed a neural network based self tuning fuzzy PID controller of which output gains were adjusted automatically. The tuning parameters of the proposed controller were determined on the basis of the conventional PID controller parameters tuning methods. Then they were adjusted by using proposed neural network learning algorithm. Proposed controller was simple in structure and computational burden was small so that on-line adaptation was easy to apply to. The experiment on the magnetic levitation system, which is known to be heavily nonlinear, showed the proposed controller's excellent performance. |