Decoupling Control of Outer Rotor Coreless Bearingless Permanent Magnet Synchronous Motor Based on RPROP Neural Network Optimized by Improved Beetle Swarm Optimization Algorithm

In order to solve the problem of strong coupling effect caused by cross coupling of the magnetic field generated by the torque windings and the suspension force windings in an outer rotor coreless bearingless permanent magnet synchronous motor (ORC-BPMSM), a decoupling control method based on an improved beetle swarm optimization (IBSO) algorithm optimized resilient back propagation neural network (RPROPNN) is proposed. Firstly, the working principle of the ORC-BPMSM is introduced and the mathematical model of the ORC-BPMSM is derived. Secondly, the system is verified to be right invertible, and the decoupling control principle of the right inverse system of the RPROPNN is explained. On this basis, the initial weights of the RPROPNN are optimized using the IBSO algorithm with dynamic learning factors and quadratic adjustment strategies for inertia weights, and the neural network inverse decoupling control system is constructed. Finally, simulations and experiments of steady-state suspension, speed variation, and anti-interference are conducted, and the results verify that the proposed IBSO-RPROPNN decoupling control method effectively improves the decoupling performance and interference resistance of the ORC-BPMSM.