Data-driven control system design, cours de Simone FORMENTIN - 23 au 25 janvier, GIPSA-lab

Abstract: For many years, the role of system identification within control projects has been that of computing from data a dynamical model of the plant under control, which is as accurate as possible. A suitable model-based controller is then designed standing on the assumption that the identified model coincides with the real system. In case of a large uncertainty, robust control can be employed to take into consideration not only the nominal model, but also a description of the uncertainty set. Unfortunately, robust controllers may lead to very conservative performance.
This course addresses the interplay between identification and control and shows that, to overcome the major limitations of model-based control, the model and the controller must be designed together. In this way, the identified model does not necessarily fit the data at best, but the related controller maximizes the closed-loop performance. As an effective alternative, the direct mapping of data onto controller parameters is also discussed.

Lieu : GIPSA-lab, bâtiment Ampère, 2e étage, en salle B208

>> Home page Simone FORMENTIN