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 inter-event time between two consecutive triggering points is proven to have a positive lower bound. At last, a simulation example is provided to show the effectiveness of the proposed approach. © 2022 IEEE.