Research on Parameter Optimization of Levitation Controller for Fast Maglev Train

Maglev train as an efficient mode of transportation, the high performance operation of its levitation system is the key to achieve stable transportation, engineering applications and commercial operations generally use PID controllers to achieve stable levitation of the levitation system. However, the traditional PID controller is difficult to adapt to the complex and changing operating conditions. In order to solve this problem, this paper mainly investigates a parameter optimization method of PID controller based on Non-dominated sorting genetic algorithm (NSGA-II). Firstly, a single-point levitation model is analyzed and established with the Changsha Maglev train as the object. On this basis, the methods of deterministic, robust and stochastic performance evaluation are analyzed and the NSGA-II control structure framework and algorithm are designed. Finally, through simulation and comparison with the particle swarm algorithm, it is shown that the optimization of the PID controller parameters using the NSGA-II algorithm can better improve the robustness of the levitation system as well as the anti-interference ability.