Paper Info
Title
Plug And Play Distributed Model Predictive Control With Dynamic Coupling: A Randomized Primal-Dual Proximal Algorithm
Abstract
This paper proposes an algorithm for distributed model predictive control that is based on a primal-dual proximal algorithm developed recently by two of the authors. The proposed scheme does not require strong convexity, involves one round of communication at every iteration and is fully distributed. In fact, both the iterations and the stepsizes are computed using only local information. This allows a plug and play implementation where addition or removal of a subsystem only affects the neighboring nodes without the need for global coordination. The proposed scheme enjoys a linear convergence rate. In addition, we provide a randomized variant of the algorithm in which at every iteration subsystems wake up randomly independent of one another. Numerical simulations are performed for the frequency control problem in a power network, demonstrating the attractive performance of the new scheme.
Year
DOI
Venue
2018
10.23919/ECC.2018.8550270
2018 EUROPEAN CONTROL CONFERENCE (ECC)
Field
DocType
Citations 
Convergence (routing),Convexity,Coupling,Linear system,Computer science,Model predictive control,Algorithm,Automatic frequency control,Plug and play,Rate of convergence
Conference
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Puya Latafat181.14
Alberto Bemporad24353568.62
Panagiotis Patrinos326831.71