Online constraint adaptation in economic model predictive control

摘要

In economic model predictive control (EMPC), the standard quadratic objective function of MPC is replaced with an economic objective such that the controller directly optimizes the economic performance of the plant. However, economic objective functions are likely to be monotone in some input direction, and this will typically lead to operation with constraints active. Operating the plant with active constraints is not economically robust; even small disturbances or errors could cause constraint violations which may lead to large costs. In this paper we address this issue by adding margins to the constraints in order to force the plant to operate in the interior of the feasible set, thereby providing some robustness to uncertainty. To determine the magnitude of these margins, we introduce an outer loop which optimizes the margins online based on measurements of the closed-loop economic performance. Our approach is simple to implement and introduces essentially no computational overhead as compared to the nominal EMPC problem. In addition, only minimal knowledge of the uncertainties present in the system is required.