Control study on support system of permanent magnetic levitation belt conveyor with controllable magnetic circuit

Abstract

This study addresses the strong nonlinearity and control challenges in the support system of a controlled magnetic circuit permanent magnetic levitation belt conveyor. We proposed a double closed-loop controller with angle and position loops to improve control accuracy and stability. The system’s torque coefficient k_t and leakage compensation coefficient Δz_τ were analyzed using the finite element method (FEM), leading to the development of a torque model. Based on this model, we established the kinetic equations for a single-point controllable permanent magnet support system by the system’s levitation force model. The control model was then derived according to the theory of double closed-loop control. To enhance the system performance, applied the Kalman filtering (KF) algorithm to filter and reduce the noise of the displacement signal. We further optimized the KF parameters using a political optimization (PO) algorithm This approach effectively suppressed output value fluctuations and reduced estimation errors. The proposed control strategy was validated through simulation tests and an experimental platform. Results showed that the dual closed-loop proportional-integral-derivative control using PO + KF offered faster response times, better tracking performance, stronger robustness, and improved steady-state errors. These findings provide a theoretical and experimental foundation for research on low-resistance belt conveyor control systems.