Stabilized Neighborhood Optimization based Distributed Model Predictive Control for distributed system

A class of large scale systems, which is naturally divided into many small interacting subsystems with constraints, are usually controlled by a distributed or decentralized control framework. Distributed Model Predictive Control (DMPC), in which each subsystem is controlled by a Model Predictive Control (MPC), is a method of choice in the venues of explicitly accommodating constraints, less computational cost and high flexibility. The stability and global performance improvement are the difficulties of DMPC. As an extend of previous work of Neighborhood Optimization based distributed MPC (NO-DMPC), a stabilized NO-DMPC (SNO-DMPC) is proposed. In each subsystem-based MPC, not only the performance of itself but also the performance of its´ neighbors are taken as cost function to improve the dynamic performance of entire system with less communication and computational cost. The consistency and stabilization constraints, as will as the bound of them are designed and the dual mode strategy is adopted to guarantee the closed-loop system stabilization. Provided an initially feasible solution can be found, subsequent feasibility of the algorithm is guaranteed at every update, and the asymptotic stabilization is established.