Simulation control analysis of a magnetic levitation flywheel rotor system based on a fuzzy PID control algorithm

Magnetic levitation flywheel rotor systems encounter numerous challenges under complex nonlinear operating conditions, including air-gap instability, intense electromagnetic force coupling, as well as the significant amplification of external disturbances. Existing control strategies frequently suffer from issues related to poor adaptability and insufficient robustness when dealing with the inherent complexities and uncertainties of these high-speed rotating systems. A two-layer fuzzy Proportional-Integral-Derivative (PID) optimization method is proposed in this study by integrating physical mechanism constraints with an event-triggered strategy to improve overall system performance. This approach enhances the response capability to nonlinear variations by embedding the electromagnetic air-gap energy function into the adaptive adjustment of membership functions while simultaneously reducing computational redundancy. Experimental results demonstrate that the optimized fuzzy PID outperforms conventional PID, Neural Network PID, and Deep Reinforcement Learning methods regarding frequency response, steady-state accuracy, and dynamic stability. This study provides a novel solution for the efficient control of maglev flywheel systems and offers valuable theoretical guidance for the control design of other complex nonlinear systems.