"Cooperative Model Predictive Control with Resource Management to Address Coupled Constraints"
Brett Stewart, University of Wisconsin-Madison
Abstract:
In chemical plants, production often involves a series of unit operations interconnected through material and energy flows. Traditionally, in plants for which centralized control is judged impractical or unmanageable, the unit operations are controlled in a decentralized way so that closed-loop interactions between controllers are neglected. It is well known, however, that if these interactions are strong plantwide performance is poor.
Cooperative model predictive control (MPC) has been proposed as a method for coordinating multiple optimization-based controllers. In this control strategy, input trajectories are passed between controllers, each optimizing a plantwide objective. It has been shown that cooperative MPC is provably stable and is plantwide optimal at convergence. A necessary assumption for optimality is that each input is constrained independently, so that the plantwide feasible space is a Cartesian product of the feasible subspaces. This assumption is not valid, however, for plants in which the constraints between controllers are coupled. This situation arises if a common resource must be optimally shared between unit operations.
We propose an auxiliary optimization that enhances cooperative MPC to manage coupled constraints. This resource manager decouples these constraints by computing the optimal inner hyperbox contained within the plantwide feasible space. The optimization proceeds asynchronously and can be terminated at a suboptimal iterate. We show the augmented optimization does not weaken the cooperative MPC stability properties and is plantwide optimal at convergence. We provide some examples showing performance properties.
Slides
