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

Résumé fourni par la source

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.

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