Paper Info
Title
Distributed Model Predictive Control Of Linear Discrete-Time Systems With Coupled Constraints
Abstract
This paper proposes a Distributed Model Predictive Control (DMPC) approach for a family of discrete-time linear systems with local (uncoupled) and global (coupled) constraints. The proposed approach is based on the dual problem of an overall MPC optimization problem involving all systems. This dual problem is then solved distributively by converting it into a consensus problem for the dual variables associated with the coupled constraints. As the state of convergence is difficult to ascertain, the distributed consensus algorithm yields an inexact solution. By the tightening of the coupled constraints, but not the local constraints, it is still possible to ensure recursive feasibility and exponential stability of the overall closed-loop system. The approach requires that the network of systems be connected and hence, local communications among the systems are needed. The performance of the proposed approach is demonstrated by a numerical example.
Year
Venue
Field
2016
2016 IEEE 55TH CONFERENCE ON DECISION AND CONTROL (CDC)
Convergence (routing),Consensus,Mathematical optimization,Linear system,Computer science,Control theory,Model predictive control,Exponential stability,Discrete time and continuous time,Optimization problem,Numerical stability
DocType
ISSN
Citations 
Conference
0743-1546
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Zheming Wang1308.12
Chong-Jin Ong271656.26
Geok Soon Hong3365.89