Paper Info
Title
A Fast Dual Gradient Algorithm For Distributed Model Predictive Control With Coupled Constraints
Abstract
This paper proposes a 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 which is then solved distributively using a modified distributed Nesterov-accelerated-gradient algorithm. To further reduce the computational requirement, this approach allows for early termination of the distributed gradient algorithm. This is made possible via a consensus algorithm that determines the satisfaction of the termination condition and by appropriate tightening of the coupled constraints. Under reasonable assumptions, the approach is able to produce a suboptimal solution so long as the network of the systems is connected while ensuring recursive feasibility and exponential stability of the closed-loop system. The performance of the proposed approach is demonstrated by a numerical example.
Year
Venue
Field
2017
2017 IEEE 56TH ANNUAL CONFERENCE ON DECISION AND CONTROL (CDC)
Convergence (routing),Mathematical optimization,Linear system,Computer science,Control theory,Model predictive control,Algorithm,Exponential stability,Duality (optimization),Convex function,Optimization problem,Numerical stability
DocType
ISSN
Citations 
Conference
0743-1546
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Zheming Wang1308.12
Chong-Jin Ong271656.26