This paper combines the advantages of the grey prediction theory, fuzzy theory to design a rule adaptive grey Prediction fuzzy controller. These different forecasting step sizes are generated by a rule adaptive mechanism with the technology of fuzzy theory. The rule adaptive grey prediction fuzzy controller structure is proposed so that the rise time and the overshoot of the controlled system can be maintained simultaneously. Finally, the inverted pendulum control problem is used to illustrate the effectiveness of the proposed control scheme. This paper presents a grey–fuzzy predictive controller that is based on fuzzy theory, grey prediction and on-line switching algorithms. The grey predictor is applied to extract key information and reduce the randomness of the measured non-stationary time-series signals from sensors, and send the prediction information to the fuzzy controller. The complete mathematical model is derived and the sufficient condition for convergence is given. To achieve better transient performance and steadystate responses, an on-line switching mechanism is adopted to regulate appropriately the forecasting step size of the grey predictor, according to the error feedback from different periods of the system response.
