Single Point Suspension Control of Maglev Train Based on BP Neural Network

Suspension control is the key technology of maglev train system. At present, the commonly used algorithm is PID control based on current feedback. However, due to the time-varying uncertainty of maglev train operation and the nonlinearity of maglev system itself, PID control is difficult to achieve the ideal control effect, and there are some problems, such as small parameter stability range, large overshoot and so on. This paper studies the application of BP neural network in suspension control system, designs PID controller based on BP neural network, and realizes on-line self-tuning of PID parameters. The simulation results in Matlab / Simulink show that compared with the simple PID control, the response speed and overshoot have been greatly improved, and the control performance of the suspension system has been improved.