A Dynamic Event-Triggered Model-Free Adaptive Iterative Learning Control with High-Order Estimation

A Dynamic Event-Triggered Model-Free Adaptive Iterative Learning Control method based on Pseudo Partial Derivative (PPD) is proposed for resource-constrained nonlinear repetitive systems. The algorithm employs a data-driven framework that requires no precise system model. Its core innovation lies in designing a PPD-associated dynamic event-triggered mechanism, which adaptively adjusts the triggering thresholds through real-time monitoring of the rate of change in the PPD estimates. Furthermore, This control architecture is realized through the integration of two elements: a feedback loop utilizing model-free adaptive control with high-order PPD estimation, and a feedforward loop driven by iterative learning control. This controller design not only enhances system stability but also compensates for repetitive nonlinearities and unknown time-varying dynamics and disturbances. Finally, simulation results demonstrate that, compared to traditional time-triggered and fixed-threshold event-triggered methods, the proposed algorithm effectively reduces the number of control parameter updates while maintaining comparable high-precision tracking performance, thereby achieving an optimal balance between control performance and communication resource consumption.