[[alternative]]Design and Application of Difference Grey Prediction Controller

[[abstract]]In grey prediction controller, there are two factors will effect the performance in control. Each of them is the precision of grey modeling and the adjustment of grey prediction step. In the grey modeling precision, the traditional GM(1,1) uses some hypothesis and mathematical approximation to transfer the solution of continuous differential equation to discrete difference equation in modeling process. Because of the forecasting sequence numbers of grey prediction are usually discrete sample data and it is easy to produce the error for the transform of continuous system to discrete system. Therefore, our research proposes the difference equation to replace the differential equation in traditional grey modeling in GM(1,1). Also, our research uses the genetic algorithm to adjust the parameters in our model. The other factor of grey prediction controller that will effect the output performance is the adjustment of the prediction step. Many researches point out that different steps will cause different performance. Hence, our research will design a dynamic method to make an appropriate prediction step. Finally, our research will combine the difference grey modeling technique and the prediction step that using dynamic method to design a difference grey prediction controller. To verify the difference grey prediction controller, our research applies it to DC servomotor for speed control in practical. Keywords : grey prediction、GM(1,1)、grey modeling、genetic algorithm difference grey modeling