OPTEC - K.U.Leuven Center of Excellence: Optimization in Engineering

"Stochastic Model Predictive Control"
Mark Cannon, University of Oxford

Stochastic Model Predictive Control is emerging as an area of research of significant theoretical interest which addresses problems of practical importance. Model Predictive Control (MPC) is recognized as the methodology of choice for optimizing closed loop performance in the presence of constraints, and it is unique in providing computationally tractable optimal control laws by solving constrained receding horizon control problems online. However, most real life applications are not only subject to constraints but also involve multiplicative and/or additive stochastic uncertainty. Earlier work tended to ignore information on the distribution of model uncertainty, and as a result addressed control problems suboptimally using robust MPC strategies that employ only information on bounds on the uncertainty. This talk will discuss strategies for handling probabilistic constraints for the case of uncertain linear systems, and will present methods of optimizing performance subject to this type of constraint, and also demonstrate that constraint satisfaction is achieved in closed loop operation. The solution of a mixed objective control problem involving coupled algebraic Riccati equations (CARE) will be described, and the convergence properties of the associated constrained receding horizon control problem presented. The results are illustrated by a design study considering control of a wind turbine in order to maximize power capture subject to constraints on fatigue damage.