High-precision synchronous control for multi-motor system: an event-triggered model predictive iterative learning approach

Abstract

Multi-motor synchronous control (MMSC) is essential for the integrated robotic joint, where synchronization performance directly impacts accuracy and stability in space-constrained applications. Traditional control methods often overlook this coupling and fail to balance disturbance rejection with energy optimization—a critical challenge for computationally constrained embedded measurement and control platforms. To address this, this paper proposes an event-triggered model predictive iterative learning (EMPIL) strategy to achieve high-precision angle tracking and coordinated control. Specifically, the scheme achieves dynamic decoupling through lumped uncertainty modeling and active compensation of differential dynamics. Simultaneously, a complementary mechanism utilizing iterative learning control and terminal integral sliding mode control suppresses diverse disturbances to ensure tracking precision without strict reliance on precise measurements. Furthermore, an event-triggered mechanism embedded in the receding horizon framework breaks the traditional periodic model predictive control paradigm. This eliminates computational redundancy and redundant communication overhead to realize an on-demand match between energy efficiency and tracking precision. Simulation and experimental results, incorporating external measurements from a motion-capture system, confirm that the proposed method achieves superior performance in angle tracking accuracy, coordination consistency, and energy efficiency.