Price-Driven Coordination of Distributed Model Predictive Controllers: A Bi-Level Optimization Approach

Abstract

Chemical and petrochemical plants typically integrate a number of geographically distributed operating units, which are physically linked through energy and material streams or inherently coupled via plant-wide constraints. The main drawback of the current decentralized control system is that it fails to consider the interrelations between subsystems, which could usually result in poor performance or even loss of closed-loop stability. Such concerns have motivated various control strategies to tackle these problems. One possibility is to replace the whole network with a centralized control structure. Despite the potential benefits, this renovation would require significant capital cost, increase maintenance costs, and reduce fault tolerance. Another practical approach is a distributed control that aims to improve the performance of a currently installed decentralized network. Distributed model predictive control (DMPC) methods are divide into two general categories: non-coordinated and coordinated schemes. Coordinated DMPC (CDMPC) networks, which consist of distributed controllers and a coordinator, are able to attain an overall optimal solution over a wide range of conditions. The focus of this thesis is to develop on-line strategies for CDMPC systems and overcome existing issues with global convergence and stability of closed-loop systems, under price-driven CDMPC concept. In particular, the main contributions are developing two novel information flow mechanisms for CDMPC of nonlinear systems and proposing a new solution method for CDMPC of linear systems, via a bi-level optimization framework.