Design of predictive fuzzy control system using GESA-based grey predictor

The grey prediction fuzzy control (GPFC) scheme consists of two main parts: the grey predictor and the fuzzy logical controller (FLC). For guaranteeing stability and obtaining optimal control performance, it is necessary to improve the accuracy of the grey predictor. Guided evolutionary simulated annealing (GESA) is a robust and rapid optimization technique, which combines the ideas of simulated evolution and simulated annealing in a novel way. In this paper, the application of GESA to search for optimized parameter values of the grey model is investigated. Then, combining the GESA-based grey predictor with FLC for a ball-suspension control system is demonstrated. The results indicate that the proposed method can improve the prediction accuracy of the grey model and can be applied to the scheme of GPFC.