Study on Hybrid Fuzzy Controller Design by Model Reduction Method

Abstract

Background/Objectives: In this paper, we proposed an improved model shrinking method and a hybrid-smith prediction fuzzy control design using a reduced model.Methods/Statistical analysis: The method of model reduction is based on Nyquist curve of frequency response, and the reduced model is obtained by considering the response of the transient state and the response of the steady state to the method. The proposed hybrid-smith prediction fuzzy controller tuning method was able to obtain the parameter value by utilizing a reduced model and using a genetic algorithm.Findings: The optimum PID controller design method using the reduced model applied a reduced model and a Smith prediction structure that compensates for the delay time in order to improve the control performance, and as a result, in order to minimize the performance index ITAE value I was able to design a controller. Here, the value of the control parameter was used by combining a method of directly obtaining the reduced model and a method of using the genetic algorithm by numerical analysis. In conclusion, the design method of the hybrid-smith Fuzzy control is a method of controlling by combining the PID controller, the Smith prediction compensating the delay time and the fuzzy controller in parallel, the value of the PID control parameter is optimized using the reduced model The value of the PID parameter was determined. The conversion coefficients (GE, GD, GH, GC) of Fuzzy control were obtained by applying genetic algorithm.Improvements/Applications: In the proposed method, it is possible to obtain directly the value of the parameter of the optimum PID controller and the value of the Smith prediction by using the reduced model, and the part of the Fuzzy conversion coefficient can be obtained by using the genetic algorithm , The ITAE performance index improved more than the conventional method.