The application provides a UAV-InSAR-based
magnetic suspension system multi-point collaborative
reinforcement learning compensation control method,
system, device and medium, the method comprises the following steps: using a UAV to carry a lightweight X-band
interferometric synthetic aperture radar system to carry out high-precision surveying and mapping on a magnetic suspension line, and constructing an
orbit irregularity power spectrum model; a
hierarchical control system is constructed, the amplitude saturation controller in the lower layer generates a basic electromagnetic force control law according to the state quantity of the current suspension point; the upper layer compensation controller based on
reinforcement learning is an agent, interacts with the environment composed of the suspension frame, the track and the amplitude saturation controller, trains by using the standardized irregularity function obtained from the track irregularity power spectrum model, and outputs the compensation control quantity for each suspension point; the basic electromagnetic force control law and the compensation control quantity are superimposed to generate the total electromagnetic force control law of each suspension point, and the suspension
system is actively compensated and globally stably suspended. The application significantly improves the comprehensive control performance of the suspension system.