Artificial Basis Functions in Adaptive Control for Transient Performance Improvement

This paper presents a new adaptive control architecture to achieve stabilization and command following of uncertain dynamical systems with improved transient performance. The proposed framework is predicated on a new and novel controller architecture involving an artificial basis function in the update law. Specifically, the proposed artificial basis function allows to shape the system error, which is between the uncertain dynamical system and a reference system capturing a desired closed-loop dynamical system behavior, during the learning phase of an adaptive controller for improving the transient performance. The efficacy of the proposed architecture is illustrated on a numerical example.