Global stabilizing distributed model predictive control systems with limited communication
ZHENG Yi
LI Shao-yuan
WEI Yong-song
Abstract：A novel stabilized distributed model predictive control (DMPC) with input constraints and global cost optimization coordination strategy is proposed for spatially distributed coupling systems which are presented by states interacted models. The distributed controllers make decisions locally and merely communicate once a control period with each others. Cooperation is promoted by consideration of the system-wide objective by each local controller. Consistency 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. The proposed control algorithm could reduce the communication and computation loads with improved performance of entire systems, and guarantee the recursive feasibility and the asympto-tically stability even when the controlled subsystems are strong coupled. Simulation results show that the performance of the proposed DMPC is very close to that of a centralized model predictive control (MPC).
Keywords：large-scale systemsmodel predictive controldistributed model predictive controlconstrained control
Publication Date：2017-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：11( 575-585 )