Research on control method of Maglev vehicle-guideway coupling vibration system based on particle swarm optimization algorithm

To solve the problem of vehicle-guideway coupling vibration, a new control approach for the Maglev vehicle-guideway coupled system was investigated. A simplified model of the system was built and a control strategy based on full state feedback and particle swarm optimization algorithm was designed. The robustness of the system considering different track stiffness and the maximum voltage of the magnet were considered when the cost function of the particle swarm algorithm was designed. A real-time test rig using dSPACE was built to test the control strategy. The result from the test rig shows that the new designed control strategy can keep the system stable and has a better response than the traditional linear quadratic optimal method, the control voltage is much lower, the settling time of step response is decreased and the maximum overshoot of the air gap is decreased more than 88%. The robustness of the system in different track stiffness conditions is also much better; that is, when the magnet and the track move relative to each other, the maximum amplitude of vibration of both the track and the magnet is 40-70% lower, and the oscillation caused by the shifting of the track beam converges much more quickly.