Learning State-Dependent Switching System Using Expectation-Maximization Algorithm

Renzi Wang

KU Leuven

Tuesday, October 17, 2023, 11:00 - 23:59

Room 01-012, Georges-Köhler-Allee 102, Freiburg 79110, Germany

Obtaining a realistic and computationally efficient model will significantly enhance the performance of a model predictive controller. This is especially true for complex scenarios where the system being controlled must interact with other systems. This work aims to construct such a model for controlling a stochastic system with the presence of decision-dependent distribution. More specifically, the stochastic system is modeled as a switching system with state-dependent switch probabilities. The parameter of both the switching probability and the sub-systems within the switching model will be identified using data. We demonstrate that the proposed method tackles the nonconvex optimization problem through an iterative approach. At each iteration, the problem is transformed into convex optimization problems that can be solved in parallel.