Parameters Optimization for suspension system of maglev train via improved PSO

The control strategy for suspension system is of great importance to stability and safety of the medium-speed maglev train. The medium-speed maglev train usually adopts the inner current loop control, causing low control accuracy and difficulty to achieve the desired effect. On basis of the in-depth comprehension of PID control and particle swarm optimization algorithm (PSO), the control strategy of PID control based on improved PSO is proposed. Through the introduction of PID control on the basis of the traditional double-loop control of the medium-speed maglev train, the optimal PID parameters are searched in the solution space by the PSO algorithm based on decreasing inertia weight. The proposed control strategy improves the efficiency and accuracy. Meanwhile, the simulation results confirm the effectiveness of the control strategy.