Particle swarm optimization based active disturbance rejection control for maglev train suspension gap

To address such issues as uncertain internal and external disturbances, open-loop instability, and the difficulty of controller parameter tuning in the electromagnetic suspension systems of maglev trains, a suspension gap control method was proposed active disturbance rejection control (ADRC) based on particle swarm optimization. The internal disturbances (e.g., model linearization errors, parameter perturbations) and external disturbances (e.g., track irregularities, sudden load changes) were treated as a unified total disturbance. An extended state observer was employed to estimate and compensate for them in real time. While the dynamic linearization of the system was achieved, a novel integrated discrete time-optimal control function was designed, and the corresponding tracking differentiator was established to enhance signal tracking performance. In response to the difficulty in manually obtaining optimal solutions for key parameters such as observer bandwidth and controller gain in the active disturbance rejection controller, the particle swarm optimization algorithm was introduced. By constructing a multi-objective fitness function incorporating the integral of absolute error and control energy consumption, the global optimization was carried out for controller parameters to balance the fast tracking capability and the noise suppression ability of the system. The research results demonstrate that under conditions of suspension startup, sudden load changes, and random track noise, the proposed method exhibits significant performance advantages over traditional manually tuned ADRC. The settling time during the startup process is significantly shortened with no overshoot. Under sudden applied load disturbances, the cumulative absolute error of the suspension gap is reduced by 28.3%, and a steady state can be recovered more rapidly. Furthermore, in a noisy sensor environment, the control voltage curve remains smooth without high-frequency chattering. This method effectively resolves the conflict between the rapidity and disturbance rejection of the maglev system, and realizes the optimal tuning of control parameters, providing an effective technical solution for the stable suspension of maglev trains under complex operating conditions.