Event-Triggered Adaptive Control of Nonlinear Systems With Dynamic Uncertainties: The Switching Threshold Case

This brief considers the adaptive tracking problem for a class of uncertain nonlinear systems. First, the disturbance observer (DO) is established, and the neural network (NN) technique is adopted to deal with unmeasured disturbances and system dynamic uncertainties. Then, the switching threshold event-triggered mechanism is utilized, and the adaptive tracking controller is further designed based on backstepping techniques. We show that the tracking errors simultaneously converge to an adjustable compact set of origin, and all the closed-loop signals are semi-globally uniformly bounded. Finally, the practicability and effectiveness of the proposed control approach are well illustrated by a simulation case.