A Control Method for Improved Fuzzy PID of GSOM for Fish Scale Evolution

Abstract

Fuzzy PID control is a control method with good adaptability and stability in complex environments. It is used to achieve precise regulation and stable control of the system. In this paper, a fish scale evolution GSOM is proposed to improve the control method of fuzzy PID. Firstly, the fish scale regulation system is established and the differential evolution theory is introduced to realize the evolutionary upgrading of the system. Secondly, the GSOM module is introduced. The system is optimized by self-organized mapping neural network to achieve dynamic regulation of polymorphic inputs. Meanwhile, the fuzzy rule base and parameter regulation mechanism in fuzzy PID control are dynamically optimized. Improve the performance of the control system. Finally, the control method of improved fuzzy PID for fish scale evolution GSOM is simulated using MATLAB. The simulation experiments also compare several traditional PID control methods. The comparison indexes include stability, robustness, control accuracy and feedback output effect. The results show that the method in this paper is more stable and has fewer iterations when facing the dynamic input environment. The tracking error and control output of the controller system are significantly improved. It has good feedback output effect, solves the saturation problem and has higher control accuracy.