::Speaker Marco Forgione ::Title Data-driven model Improvement for Model-based Control ::Abstract In this talk, I present a framework for the gradual improvement of model-based control for linear systems operating in closed-loop. The total time of closed-loop operation is divided into a number of consecutive learning intervals. After an interval is executed, the model is refined based on the measured data using system identification techniques. This model is used to synthesize the controller applied during the next interval. Excitation signals can be injected into the control loop during each of the learning intervals. On the one hand, the presence of an excitation signal worsens the control performance in the current interval since it acts as a disturbance. On the other hand, the more informative data generated with the excitation signal applied lead to a more accurate model after re-identification. Therefore, the control performance for the next intervals is likely to improve. In principle, the excitation signals should be designed to maximize the overall control performance taking this dual effect explicitly into account. However, this is in an intractable stochastic optimization problem. For this reason, a convex approximation of the original problem is derived. The approximated problem can be solved efficiently using standard optimization software. The applicability of the method is demonstrated in a simulation study and preliminary extensions to nonlinear systems are discussed in the final part of the presentation. *Bio: Marco Forgione is a researcher in the Control and Automation field. He currently works in the industrial sector. He holds a M.Sc. degree in Computer Engineering from the University of Pavia, Italy. Postdoctoral Researcher at the Ecole Centrale de Lyon, *Location: IDSIA's meeting room