An Anti-Singularity Data-Driven-Control-Based Method for Soft Start and Multi-Machine Balance of Magnetic Couplers

The speed-regulating magnetic coupler faces challenges in mechanism modeling for constant-torque-load soft start and multi-motor power balance. Moreover, system data contain singular noise values. To address these issues, an anti-singularity data-driven control strategy is proposed. This strategy enhances speed control accuracy and operational robustness. The method designs an exponentially stable air-gap regulation law using Lyapunov theory. A robust data model is constructed by estimating angular acceleration via piecewise least squares with singularity screening, followed by model extension using a generalized distance weighting factor, which enables the numerical solution of the control law. Experimental results demonstrate a speed control accuracy within 4%. In practical applications on long-distance belt conveyors, the strategy achieves a soft-start acceleration of ≤0.15 m/s2 with a 25 s start-up time, maintains power balance among motors within a 5% deviation, and improves energy efficiency by 17.6%. This work provides an effective data-driven solution for the high-performance control of magnetic couplers in complex industrial scenarios.