Journal
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
Volume 12, Issue 4, Pages 1447-1460Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TASE.2014.2337259
Keywords
Constrained control; distributed model predictive control; impacted-region optimization; large-scale systems; model predictive control; plant-wide optimization
Categories
Funding
- National Nature Science Foundation of China [61233004, 61221003, 61374109, 61304078]
- National Basic Research Program of China (973 Program) [2013CB035500]
- International Cooperation Program of Shanghai Science and Technology Commission [12230709600]
- Higher Education Research Fund for the Doctoral Program of China [20120073130006, 20110073110018]
- China Postdoctoral Science Foundation [2013M540364]
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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 sub-system-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.
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