Simulation of a fuzzy-PD learning control system

Conventional PD-type controllers have poor performances when dealing with nonlinear plants outside the linear domain for which they have been designed. In this paper we apply a learning algorithm to a fuzzy-PD controller to improve its performance in controlling a nonlinear plant. The previously designed fuzzy-PD control system is adapted by a learning mechanism which adjusts the controllerpsilas rule base so that the closed loop system behaves according to a reference model. The performances of the presented control structure, as applied to a nonlinear electromagnetic levitation system, are evaluated and shown by means of digital simulation.

[1]  Jan Jantzen,et al.  Tuning Of Fuzzy PID Controllers , 1998 .

[2]  Stephen Yurkovich,et al.  Fuzzy Control , 1997 .

[3]  Bohdan S. Butkiewicz,et al.  About Robustness of Fuzzy Logic PD and PID Controller under Changes of Reasoning Methods , 2002, Advances in Computational Intelligence and Learning.

[4]  K.M. Passino,et al.  Fuzzy model reference learning control for cargo ship steering , 1993, Proceedings of 8th IEEE International Symposium on Intelligent Control.

[5]  Anuradha M. Annaswamy,et al.  Robust Adaptive Control , 1984, 1984 American Control Conference.

[6]  Kevin M. Passino,et al.  Fuzzy model reference learning control for cargo ship steering , 1993 .

[7]  Jian-Xin Xu,et al.  Fuzzy PD iterative learning control algorithm for improving tracking accuracy , 1998, SMC'98 Conference Proceedings. 1998 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.98CH36218).

[8]  FAYEZ F. M. EL-SOUSY,et al.  PID-Fuzzy Logic Position Tracking Controller for Detuned Field-Oriented Induction Motor Servo Drive , 2004 .