Event-Triggered Model Reference Adaptive Control for Linear Partially Time-Variant Continuous-Time Systems With Nonlinear Parametric Uncertainty

In this work, we develop an event-triggered adaptive control approach for solving the state tracking problem of linear partially time-variant continuous-time systems with the nonlinear state-dependent matched parametric uncertainty under unknown system dynamics. First, an event-triggered model reference adaptive controller is designed, which is composed of event-triggered adaptive laws based on the event-updated information and an event-triggering condition depending on the state tracking error of the controlled plant and reference model. Then, the state-tracking error and the error between control parameters and ideal ones of the resulting closed-loop system are proven to be uniformly ultimately bounded. Moreover, based on the designed event-triggering condition, the interevent time between two consecutive triggering points is proven to have a positive lower bound. Finally, a simulation example is provided to show the effectiveness of the proposed approach.