Adaptive backstepping dynamic sliding mode control for maglev turning motor spindle based on limited time preset performance

Abstract

In the process of turning a maglev motorized spindle, there are problems such as system model time-varying, cross-coupling of control parameters, difficult measurement of system state variables, and nonlinear characteristics of active magnetic bearings, which lead to the inevitable cutting chatter phenomenon, difficult control algorithm design, and then the reduction of workpiece surface quality and accuracy, affecting machining efficiency and tool life. In this paper, the “multimodal-distributed parameter” model is extended to the “magnetic bearing-rotor-workpiece” variable mass turning process. An adaptive backstepping fast dynamic terminal sliding mode control is designed to address the model’s time-varying parameters and cross-coupling issues. In view of the difficulty in measuring the vibration displacement at the cutting point, the displacement field reconstruction method was introduced to reconstruct the vibration displacement field online and provide effective feedback for the previously designed control strategy. Finally, the proposed controller is applied to adjust and control the turning process of a maglev motorized spindle and compared with other advanced controllers. The simulation results show that the proposed control method has a better control effect than other control methods in the presence of unmodeled dynamics, uncertainties, and external disturbances.