Vibration Control for Active Magnetic Bearing Rotor System Based on Parameter Adaptive-Particle Swarm Optimization and Notch Filter

This article proposes a control strategy integrating parameter adaptive particle swarm optimization (PA-PSO) and adaptive notch filter to address the challenges of residual imbalance vibration suppression and dynamic stiffness enhancement in the active magnetic bearing (AMB) rotor system under high-speed operating conditions. The four-degree-of-freedom rigid rotor dynamics model reveals the unbalanced force perturbation of the rotor caused by the unbalanced mass, as well as the mechanism of suppressing the resonance peaks in the critical speed region by the notch filter parameters. An innovative asymmetric collaborative optimization mechanism based on the Sigmoid function is introduced, which dynamically adjusts the inertia weight and learning factors to balance global exploration and local exploitation. Additionally, feedback on the fitness change rate and particle displacement constraints is incorporated to effectively suppress premature convergence of the algorithm. The parameters of the notch filter are optimized to suppress the resonance response in the critical speed region to ensure stable operation of the rotor at high speeds. Simulation and experimental results show that the method proposed significantly enhances the antiinterference ability while ensuring system stability, offering a novel approach for active vibration control in AMB-rotor system.