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The suspension regulation is critical to the operation of medium-low-speedmaglev trains (mlsMTs). Due to uncertain environment, strong disturbances andhigh nonlinearity of the system dynamics, this problem cannot be well solved bymost of the model-based controllers. In this paper, we propose a model-freecontroller by reformulating it as a continuous-state, continuous-action Markovdecision process (MDP) with unknown transition probabilities. With thedeterministic policy gradient and neural network approximation, we designreinforcement learning (RL) algorithms to solve the MDP and obtain astate-feedback controller by using sampled data from the suspension system. Tofurther improve its performance, we adopt a double Q-learning scheme forlearning the regulation controller. We illustrate that the proposed controllersoutperform the existing PID controller with a real dataset from the mlsMT inChangsha, China and is even comparable to model-based controllers, which assumethat the complete information of the model is known, via simulations.

Suspension Regulation of Medium-low-speed Maglev Trains via Deep Reinforcement Learning