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A bearingless permanent magnet-type synchronous motor (BPMSM) is a complicated non-linear and strongly coupled system. The decoupling control among the electromagnetic torque and radial suspension forces, and the dynamic decoupling control between the two orthogonal suspension forces are key techniques for the stable operation of a BPMSM. In this paper, a comprehensive system model, which consists of two Park inverse transformations, two Clark inverse transformations, two current following inverters and load model of BPMSM is given. The reversibility of the complex-controlled object is proved. Combining an artificial neural network (ANN) based inverse model, which consists of a static ANN and five integrators with the controlled object, the control system is decoupled into two independent second-order linear subsystems and a first-order linear subsystem, i.e., two displacement subsystems and a rotor speed subsystem. It then becomes much easy to design the close-loop linear regulators to control each of the subsystems. The simulation test results have shown that the proposed method can achieve strong robustness, good static and dynamic decoupling performance.

The state equations of the active magnetic bearing (AMB) multivariable system are established, and the configuration and working principle of active disturbances rejection controllers (ADRCs) are introduced. Based on the principle of ADRC and the mathematics model of AMB, an active disturbances rejection decoupling control strategy for the AMB multivariable system is put forward. The configuration of control system is built and the corresponding control arithmetic is given. According to the parameters of the experiment rig, the decoupling control simulation system is set up with MATLAB software. The step response of system, the start-up experiment of the rotor and the system antidisturbance performance are simulated. The simulation results have shown that the decoupling control system succeeds in decoupling control of the AMB multivariable system and has good static and dynamic performance.