Distributed Model Predictive Control of Nonlinear Systems Based on Price-Driven Coordination

Here, a nonlinear plant is considered, which is operated by a decentralized control system. The existing system ignores the interactions between subsystems, which often results in uncaptured plantwide performance. The focus of this paper is on the design of a distributed model predictive control (DMPC) network using successively linearized internal models. In this method, all existing interactions between the subsystems should be considered in order to enhance the performance of the current decentralized DMPC scheme. A coordination layer is added to the existing network, while minor modifications are applied to the local MPC controllers, to achieve the performance and stability of a hypothetical centralized MPC for the entire plant. In this work, an interior-point algorithm is proposed to coordinate a DMPC network via the price-driven coordination approach. In addition, the convergence of the algorithm is shown, and the necessary conditions to ensure the closed-loop stability of the system are provided for the situation when the algorithm is terminated prematurely prior to convergence.