Research on Intelligent Integrated Detection System with Fiber Optic Imaging in High Voltage Switchgear Environment

This study aims to enhance the response speed and accuracy of nonlinear fuzzy control systems through accelerated evolutionary programming. Nonlinear systems are pervasive in engineering applications, where their inherent complexity and uncertainty pose significant challenges to traditional control methods. Fuzzy control, an intelligent approach based on empirical rules, is adept at handling uncertainty and nonlinear issues. However, conventional fuzzy controllers often fall short in achieving optimal performance when addressing highly complex nonlinear systems. To address this, this paper introduces accelerated genetic algorithm (RGA) to optimize fuzzy controllers, thereby improving their performance in nonlinear systems. By integrating genetic algorithms with fuzzy adaptive PID controllers, the optimized fuzzy controller is better equipped to accommodate the dynamic variations of nonlinear systems, delivering more robust and efficient control solutions. Simulation results indicate that the RGA-optimized fuzzy control system demonstrates significant improvements in response time, stability, and error reduction. This research offers new insights and methods for further optimization of nonlinear system control and provides theoretical support for practical applications in complex systems.