Then, the controller adapts feedback gains to minimize this error signal using the information received from the update law.

From a practical standpoint, the output (resp., state) of the uncertain dynamical system can be far different from the output (resp., state) of the reference system during transient time (learning phase), although a model reference adaptive control scheme can guarantee that the distance between the uncertain dynamical system and the reference system vanishes asymptotically. This problem, so-called poor transient performance phenomenon, can be solved by increasing the learning rate of the update law, and hence, fast adaptation can be achieved in order to suppress uncertainties rapidly during transient time. However, update laws with high learning rates may yield to signals with high-frequency content, which can violate actuator rate saturation constraints and/or excite unmodeled system dynamics \[, \] resulting in system instability for practical applications. Hence, a critical trade-off between system stability and control adaptation rate exists in most adaptive control approaches, with some notable exceptions \[–\]. Specifically, the authors in [@notable:1] use a low-pass filter that subverts high-frequency oscillations attributable to fast adaptation, and their approach has guaranteed transient and steady-state performance. The authors in [@notable:2] present an adaptive control approach 