Impacted-Region Optimization for Distributed Model Predictive Control Systems With Constraints

Abstract

For a large-scale distributed system, distributed model predictive control (DMPC) is a method of choice because of its ability to explicitly accommodate constraints and to achieve good dynamic performance. In the design of a DMPC, guaranteeing stability with a strong global performance is known to be a challenge. In this paper, we consider a large-scale distributed system whose input is constrained to given sets in their respective spaces and propose a stabilizing DMPC design, where each subsystem-based model predictive control (MPC) optimizes the cost function of the entire system over the region it directly impacts on. Consistency constraints and stability constraints, which bound the estimation errors of the interaction sequences among subsystems, are designed to guarantee that, if an initially feasible solution can be found, subsequent feasibility of the algorithm is guaranteed at every update, and that the closed-loop system is asymptotically stable. A key feature of the proposed DMPC is that it coordinates the MPCs of the subsystems by redefining the impact region of a subsystem according to the coordination strategy. Simulation results show that the performance of the proposed DMPC is very close to that of a centralized MPC.