Distributed Model Predictive Control Under Inexact Primal-Dual Gradient Optimization Based on Contraction Analysis
Abstract
This paper develops a distributed model predictive control (DMPC) strategy for a class of discrete-time linear systems with consideration of globally coupled constraints. The DMPC under study is based on the dual problem concerning all subsystems, which is solved by means of the primal-dual gradient optimization in a distributed manner using Laplacian consensus. To reduce the computational burden, the constraint tightening method is utilized to provide a capability of premature termination with guaranteeing the convergence of the DMPC optimization. The contraction theory is first adopted in the convergence analysis of the primal-dual gradient optimization under discrete-time updating dynamics towards a nonlinear objective function. Under some reasonable assumptions, the recursive feasibility and stability of the closed-loop system can be established under the inexact solution. A numerical simulation is given to verify the performance of the proposed strategy.
Cite
@article{arxiv.1907.10169,
title = {Distributed Model Predictive Control Under Inexact Primal-Dual Gradient Optimization Based on Contraction Analysis},
author = {Yanxu Su and Yang Shi and Changyin Sun},
journal= {arXiv preprint arXiv:1907.10169},
year = {2019}
}