Data-driven Learning and Control - DDLC

Abstract : In stochastic control, typically, an ideal model is assumed or an estimate model is learned, and the control design is based on this model, raising the robustness problem of performance loss due to the mismatch between the assumed model and the actual system. Even when a correct model is available, computation constraints may dictate the use of approximation methods. In this talk, we will view approximations and robustness under a unified theme. Robustness may be with regard to the system model, driving noise distribution, the initial prior, or approximations as required by computational methods. We will study this problem in both discrete-time and continuous-time and under several criteria and information structures...more