Data-driven-based event-triggered tracking control for non-linear systems with unknown disturbance

A novel event-triggered model-free adaptive tracking control problem is studied for non-linear systems with unknown bounded disturbance. A general dynamic linearisation model framework with disturbance input is developed and event-triggered-based model-free adaptive control algorithm is designed by using pseudo-partial derivatives method and input/output measurement data. Owing to the existence of unknown disturbance, a disturbance estimator is designed based on the optimisation criterion technique. Then, a new event-triggering mechanism with dead-zone operator is designed to improve the utility of network communication resources without Zeno phenomenon. Then, an observer-based controller with disturbance compensation is developed, such that the tracking error between the system output and desired output signal converges to a small residual set around the origin. Finally, two simulation examples are provided to show the effectiveness and practicability of the proposed approach.