A distributed augmented Lagrangian method for model predictive control

Conference Paper

In this paper we present a distributed Augmented Lagrangian (AL) algorithm to solve model predictive control (MPC) problems that involve a finite number of subsystems which interact with each other via a general network. We focus on discrete-time control systems with time-varying linear dynamics. Our method relies on the Accelerated Distributed Augmented Lagrangian (ADAL) algorithm, which can handle globally coupled linear constraints in a distributed manner based on a locally estimated AL. We prove that the theoretical complexity of ADAL to reach an ϵ-optimal solution both in terms of primal optimality gap and feasibility residual is O(1/ϵ) iterations. As suggested by our numerical analysis, ADAL achieves very fast convergence rates compared to the popular ADMM for distributed MPC.

Full Text

Duke Authors

Cited Authors

  • Lee, S; Chatzipanagiotis, N; Zavlanos, MM

Published Date

  • January 18, 2018

Published In

  • 2017 Ieee 56th Annual Conference on Decision and Control, Cdc 2017

Volume / Issue

  • 2018-January /

Start / End Page

  • 2888 - 2893

International Standard Book Number 13 (ISBN-13)

  • 9781509028733

Digital Object Identifier (DOI)

  • 10.1109/CDC.2017.8264078

Citation Source

  • Scopus