Unconstrained networked decentralized model predictive control

M. Vaccarini, S. Longhi, M.R. Katebi

Research output: Contribution to journalArticle

46 Citations (Scopus)

Abstract

Complex processes are naturally suitable to be controlled in a decentralized framework: centralized control solutions are often unfeasible in dealing with large scale plants and they are computationally prohibitive when the processes are too fast for the existing computational resources. In these cases, the resulting control problem is usually split into many smaller subproblems and the global requirements are guaranteed by means of a proper coordination. A coordination strategy based on a networked decentralized Model Predictive Control is proposed in this paper for improving the global control performances. The innovative solution is based on independent agents and on a local area network used for exchanging a reduced set of information. The proposed architecture guarantees satisfactory performance under strong interactions among subsystems. A stability analysis is presented for the unconstrained decentralized case and the provided stability results are employed for tuning the decentralized controller. Numerical simulations are given for testing and validating the proposed technique.
LanguageEnglish
Pages328-339
Number of pages12
JournalJournal of Process Control
Volume19
Issue number2
DOIs
Publication statusPublished - Feb 2009

Fingerprint

Model predictive control
Model Predictive Control
Decentralized
Local area networks
Performance Guarantee
Tuning
Stability Analysis
Control Problem
Controllers
Subsystem
Computer simulation
Testing
Controller
Numerical Simulation
Resources
Requirements
Interaction

Keywords

  • decentralized control
  • model predictive control
  • stability analysis
  • fault-tolerant control
  • nonlinear processes

Cite this

Vaccarini, M. ; Longhi, S. ; Katebi, M.R. / Unconstrained networked decentralized model predictive control. In: Journal of Process Control. 2009 ; Vol. 19, No. 2. pp. 328-339.
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Unconstrained networked decentralized model predictive control. / Vaccarini, M.; Longhi, S.; Katebi, M.R.

In: Journal of Process Control, Vol. 19, No. 2, 02.2009, p. 328-339.

Research output: Contribution to journalArticle

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