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On feasibility, stability and performance in distributed model predictive control

Giselsson, Pontus LU orcid and Rantzer, Anders LU orcid (2014) In IEEE Transactions on Automatic Control 59(4). p.1031-1036
Abstract
We present a stopping condition to the duality based distributed optimization algorithm presented in [1] when used in a distributed model predictive control (DMPC) context. To enable distributed implementation, the optimization problem has neither terminal constraints nor terminal cost that has become standard in model predictive control (MPC). The developed stopping condition guarantees a prespecified performance, stability, and feasibility with finite number of algorithm iterations. Feasibility is guaranteed using a novel adaptive constraint tightening approach that gives the same feasible set as when no constraint tightening is used. Stability and performance of the proposed DMPC controller without terminal cost or terminal constraints... (More)
We present a stopping condition to the duality based distributed optimization algorithm presented in [1] when used in a distributed model predictive control (DMPC) context. To enable distributed implementation, the optimization problem has neither terminal constraints nor terminal cost that has become standard in model predictive control (MPC). The developed stopping condition guarantees a prespecified performance, stability, and feasibility with finite number of algorithm iterations. Feasibility is guaranteed using a novel adaptive constraint tightening approach that gives the same feasible set as when no constraint tightening is used. Stability and performance of the proposed DMPC controller without terminal cost or terminal constraints is shown based on a controllability parameter for the stage costs. To enable quantification of the control horizon necessary to ensure stability and the prespecified performance, we show how the controllability parameter can be computed by solving a mixed integer linear program (MILP). (Less)
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author
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organization
publishing date
type
Contribution to journal
publication status
published
subject
in
IEEE Transactions on Automatic Control
volume
59
issue
4
pages
1031 - 1036
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
external identifiers
  • scopus:84897393731
  • wos:000333530100017
ISSN
0018-9286
DOI
10.1109/TAC.2013.2285779
project
LCCC
language
English
LU publication?
yes
additional info
key=gis_ran2012tac
id
0191b660-dd4b-4afa-bf00-b80d9dd5daf5 (old id 2278081)
date added to LUP
2016-04-04 08:39:23
date last changed
2025-04-04 15:23:02
@article{0191b660-dd4b-4afa-bf00-b80d9dd5daf5,
  abstract     = {{We present a stopping condition to the duality based distributed optimization algorithm presented in [1] when used in a distributed model predictive control (DMPC) context. To enable distributed implementation, the optimization problem has neither terminal constraints nor terminal cost that has become standard in model predictive control (MPC). The developed stopping condition guarantees a prespecified performance, stability, and feasibility with finite number of algorithm iterations. Feasibility is guaranteed using a novel adaptive constraint tightening approach that gives the same feasible set as when no constraint tightening is used. Stability and performance of the proposed DMPC controller without terminal cost or terminal constraints is shown based on a controllability parameter for the stage costs. To enable quantification of the control horizon necessary to ensure stability and the prespecified performance, we show how the controllability parameter can be computed by solving a mixed integer linear program (MILP).}},
  author       = {{Giselsson, Pontus and Rantzer, Anders}},
  issn         = {{0018-9286}},
  language     = {{eng}},
  number       = {{4}},
  pages        = {{1031--1036}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  series       = {{IEEE Transactions on Automatic Control}},
  title        = {{On feasibility, stability and performance in distributed model predictive control}},
  url          = {{http://dx.doi.org/10.1109/TAC.2013.2285779}},
  doi          = {{10.1109/TAC.2013.2285779}},
  volume       = {{59}},
  year         = {{2014}},
}