A Nominal Control Structure-Agnostic Model Reference Adaptive Control Framework

In this paper, we address a limitation in the traditional model reference adaptive control literature concerning the knowledge of the structure of a closed-form nominal control signal. Having this knowledge can be limiting when considering that control signals can be created by human operators, artificial intelligence algorithms, or other approaches that do not produce that closed-form structure. In particular, we propose a nominal control structure-agnostic model reference adaptive control framework to help deal with this limitation. The proposed framework also offers user-defined performance guarantees between the reference model and uncertain dynamical system trajectories. To show the efficacy of this framework, we present a numerical example where a human subject is producing the nominal control signal for a dynamical system.