Self-Sensing Technology of Rotor Displacement for Six-Pole Radial Active Magnetic Bearing Using Improved Quantum Particle Swarm Optimized Cubature Kalman Filter

In order to solve the problems of high cost and large volume of the displacement sensors in magnetic bearings, an improved quantum particle swarm optimization (IQPSO) algorithm optimized cubature Kalman filter (CKF) prediction model was proposed for a six-pole radial active magnetic bearing (AMB). First, the structure and operation principle of the AMB are introduced, and the mathematical model of the suspension force is derived by the equivalent magnetic circuit method. Combined with CKF, the prediction model of the six-pole radial AMB is established, and the state prediction values obtained by the prediction model at the time of updating stage are optimized by the IQPSO algorithm, and the rotor displacement self-sensing technology is achieved. Then, the simulation system is constructed, and the simulation analysis of floating and anti-interference are carried out, which show the prediction ability and anti-interference performance of IQPSO algorithm optimized CKF prediction model is stronger than standard prediction model. Finally, the verification experiment is carried out on the experimental platform, which proves the feasibility of the method.