Data-driven Speed Control for Permanent Magnet DC Motors of Sewing Machines

This paper studies a data-driven speed control issue for permanent magnet DC motors of sewing machines. Firstly, a model-free adaptive sliding mode control method is developed to manage the motor effectively without relying on a predefined system model. Then, an event-triggering mechanism is integrated into this control strategy to reduce the communication burden. Moreover, compensation algorithms for both input and output are designed to mitigate the impact of data dropouts in both feedback and forward channels. Finally, the convergence of the control error is theoretically validated, and simulation studies confirm the efficiency and robustness of the proposed method.