based on high learning rates that allows fast adaptation without hindering system robustness. Even though the methodologies documented in \[, \] are promising, they require the knowledge of a conservative upper bound on the unknown constant gain appearing in their uncertainty parameterization. While this conservative upper bound can be available for some applications, the actual upper bound may change and exceed its conservative estimate, for example, when an aircraft in a flight control application undergoes a sudden change in dynamics. In such circumstances, the performance of these adaptive controllers may be poor, because tuning them online with a new conservative upper bound is not possible. The author in [@notable:3] presents a modification to the reference system in order to solve the poor transient performance phenomenon, where a detailed analysis of this approach is given in [@notable:4]. Specifically, this modification is constructed by using a modification gain multiplied by the system error that is between the uncertain dynamical system and the modified reference system. In the limit as this modification gain goes to infinity, it is shown that the system error goes to zero in transient time. This approach can be used to effectively suppress uncertainties, however, for example, in the 