Abstract: To enhance the load-carrying capacity of magnetic bearings and address a multivariable-coupled, time-varying system, a model predictive control strategy based on the asymmetric differential control approach is investigated. First, the operating principles of conventional differential control and the asymmetric differential control are comparatively analyzed. Subsequently, a model predictive controller is formulated based on the state-space model of the magnetic bearing-rotor system, and its parameters are optimized using the whale optimization algorithm to improve control performance. Thereafter, the developed control system is validated for correctness and effectiveness using Adams-Simulink electromechanical co-simulation. Finally, the designed controller is applied to a magnetic bearing flywheel rotor test rig to carry out static levitation, load-carrying capacity evaluation, and high-speed rotation experiments. Experimental results show that the asymmetric differential control approach increases the nominal load-carrying capacity of the magnetic bearing by 40.0% and the actual load-carrying capacity by 37.5%. The flywheel rotor operates stably at 12,000 r/min, with a peak current ripple below 1 A and a peak rotor vibration amplitude below 10 μm. The study demonstrates that the asymmetric differential control approach significantly enhances the load-carrying capacity of magnetic bearings without altering their mechanical dimensions, while the model predictive controller ensures satisfactory dynamic performance under the asymmetric differential control approach.

Key words: magnetic bearing, load-carrying capacity, model predictive control, whale optimization algorithm, electromechanical co-simulation