Abstract
Adaptive parameter estimation and adaptive control have been well developed for uncertain systems to improve modeling and control performance. However, the well-known parameter estimation and adaptive control methods have been mainly designed based on the gradient algorithms (with appropriate modifications) with prediction error or control error. Hence, the parameter estimation convergence and the online verification of the required persistent excitation (PE) condition are generally difficultwith this framework. In this talk, we will introduce a novel robust, fast adaptive parameter estimation framework, where the estimation error between the unknown parameters and their estimates are explicitly obtained and then use to drive online adaptation algorithms. This new adaptation even allows to achieve finite-time parameter estimation, and can be easily incorporated into adaptive control designs to achieve tracking and parameter estimation simultaneously. We will introduce an intuitive and numerically feasible approach to online verify the PE condition. Finally, several practical application of this new adaptation to in-car parameters, adaptive control design and approximate dynamic programming for robotics, vehicles, wave energy converters (WECs) and other realistic systems will be presented.
