I. Introduction
A reference model, an update law, and a controller are the main ingredients of model reference adaptive control architectures. Briefly, a desired closed-loop system performance can be captured by the reference model, where its output or state is compared with the output or state of an uncertain dynamical system to form a system error. This system error drives the update law and then the controller adapts feedback gains to suppress the system error using the information received from the update law. It is a challenge to achieve user-defined performance guarantees while utilizing model reference adaptive control laws in the feedback loop, although these controllers have the capability to cope with adverse effects resulting from exogenous disturbances and system uncertainties. To this end, the authors of this paper recently introduced a set-theoretic model reference adaptive control framework [1] and [2].