Event-Triggered H∞ Control for Permanent Magnet Synchronous Motor via Adaptive Dynamic Programming

In this work, an adaptive dynamic programming (ADP)-based event-triggered infinite-horizon (H∞) control algorithm is proposed for high-precision speed regulation of permanent magnet synchronous motors (PMSMs). The H∞ control problem of PMSM can be formulated as a two-player zero-sum differential game, and only a single critic neural network is needed to approximate the solution of the Hamilton–Jacobi–Isaacs (HJI) equations online, which significantly simplifies the control structure. Dynamically balancing control accuracy and update frequency through adaptive event-triggering mechanism significantly reduces the computational burden. Through theoretical analysis, the system state and critic weight estimation error are rigorously proved to be uniform ultimate boundedness, and the Zeno behavior is theoretically precluded. The simulation results verify the high accuracy tracking capability and the strong robustness of the algorithm under both load disturbance and shock load, and the event-triggering mechanism significantly reduces the computational resource consumption.