学术报告第201958期

摘要：Adaptive parameter estimation and   adaptive control have been well developed to achieve modeling and control for   uncertain systems. However, the well-known parameter estimation and adaptive   control methods have been mainly designed based on the gradient algorithms   (with appropriate modifications) by using the prediction error or control   error. Hence, the parameter estimation convergence and the online   verification of the required persistent excitation (PE) condition are   generally difficult with 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   control and parameter estimation simultaneously. We will also 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 estimation, ITER cryogenics system identification, and   adaptive control for robotics, wave energy converters (WECs) and other   realistic systems will be presented.