dynamical systems, they require the knowledge of uncertainty bounds. Characterization of these bounds is not trivial due to practical constraints, because it requires extensive and costly verification and validation procedures. Furthermore, in the face of high uncertainty levels, system faults, or structural damage, these approaches may fail to satisfy a given system stabilization or command following requirement. On the other hand, adaptive control design approaches can effectively deal with these sources of uncertainties and require less modeling information than do fixed-gain robust control approaches. These facts make adaptive control theory a candidate for many applications. The control framework of this work builds on a well-known and important class of adaptive controllers, specifically, model reference adaptive controllers. Whitaker *et al.* \[, \] originally proposed the model reference adaptive control concept. In particular, model reference adaptive control schemes have three major components, namely, a reference system (model), an update law, and a controller (Figure \[early:figure:1\]).

The reference system, in the classical sense, captures a desired closed-loop dynamical system behavior for which its output (resp., state) is compared with the output (resp., state) of the uncertain dynamical system. This comparison results in a system error signal used to drive the update law online. 