Suspension Control of Maglev Train Based on Extended Kalman Filter and Linear Quadratic Optimization

In recent years Maglev transport has received more and more attention because of its green, environmentally friendly and wide speed domain. The suspension control system is one of the core components of a Maglev train, and its open-loop instability, strong non-linearity and complex operating environment make the design of the control algorithm a great challenge. The suspension control system of Maglev train is plagued by noise and partial state unpredictability, and suspension stability may tend to deteriorate, so this paper proposes a suspension control method based on the extended Kalman filter algorithm to address the problem. Specifically, a mathematical model of the single-point suspension system is established firstly. Then the corresponding state observer is designed using the principle of the extended Kalman filter algorithm for the process, measurement noise and state unpredictability problems. Then the linear quadratic optimal control with feed-forward control and the extended Kalman filter are combined to propose a suspension controller suitable for the complex environment of Maglev trains. Finally, through numerical simulation, we have verified that the proposed method is able to achieve stable suspension and good dynamic performance of the system while overcoming the effects of process and measurement noise and estimating the velocity of the airgap, with an overshoot of the actual gap of approximately 0.4%, a rise time of 0.36 s, an adjustment time of 0.64 s to reach the 2% error band, and a tolerance band between the Kalman filter estimate and the actual value of only 0.13 mm.