A recurrent neural fuzzy controller based on self-organizing improved particle swarm optimization for a magnetic levitation system.

This paper proposes a recurrent neural fuzzy controller (RNFC) approach based on a self-organizing improved particle swarm optimization (SOIPSO) algorithm used for solving control problems. The proposed SOIPSO algorithm can adaptively determine the number of fuzzy rules and automatically adjust the parameters in an RNFC. The proposed learning algorithm consisted of phases of structure and parameter learning. Structure learning adopts several subswarms to constitute the adjustable variables in fuzzy systems, and an elite-based structure strategy determines the suitable number of fuzzy rules. This paper proposes an improved particle swarm optimization technique, which consists of the modified evolutionary direction operator (MEDO) and traditional PSO techniques. The proposed MEDO method used the EDO and migration operation to improve the search ability of a global solution. Finally, the proposed RNFC approach based on the SOIPSO learning algorithm (RNFC-SOIPSO) was adopted to control a magnetic levitation system. Experimental results demonstrated that the proposed RNFC-SOIPSO model outperforms other models. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]