Adaptive Regulation for Hammerstein and Wiener Systems with Event-Triggered Observations

The paper considers the adaptive regulation for the Hammerstein and Wiener systems with event-triggered observations. The authors adopt a direct approach, i.e., without identifying the unknown parameters and functions within the systems, adaptive regulators are directly designed based on the event-triggered observations on the regulation errors. The adaptive regulators belong to the stochastic approximation algorithms and under moderate assumptions, the authors prove that the adaptive regulators are optimal for both the Hammerstein and Wiener systems in the sense that the squared regulation errors are asymptotically minimized. The authors also testify the theoretical results through simulation studies.