Adaptive Terminal Sliding Mode Speed Regulation for PMSM Under Neural-Network-Based Disturbance Estimation: A Dynamic-Event-Triggered Approach

The speed regulation control issue of the networked permanent magnet synchronous motor system is investigated in this article by utilizing a nonsingular terminal sliding mode control scheme. In order to remove the demand of the prior knowledge for possible lumped disturbance and parameter uncertainties in the permanent magnet synchronous motor system, an adaptive neural network is introduced into the proposed terminal sliding mode control scheme. Moreover, in order to ease the communication overheads of the networked system, a new dynamic event-triggered mechanism with considering the neural network estimation error is employed to schedule the signal transmission between the speed sensor and the remote sliding mode controller. The Zeno phenomena for the developed dynamic event-triggered mechanism is excluded via explicit analysis. It is further shown that by choosing suitable sliding mode parameters, the proposed control strategy can guarantee the convergence of the sliding variable into a practical sliding region as well as the ultimate boundedness of the speed regulation error. Finally, the feasibility and applicability of the proposed speed regulation strategy are demonstrated by simulation results and a real experiment.