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Analytical results for the multi-objective design of model-predictive control

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Title: Analytical results for the multi-objective design of model-predictive control
Authors: Bachtiar, V
Manzie, C
Moase, WH
Kerrigan, EC
Item Type: Journal Article
Abstract: In model-predictive control (MPC), achieving the best closed-loop performance under a given computational capacity is the underlying design consideration. This paper analyzes the MPC tuning problem with control performance and required computational capacity as competing design objectives. The proposed multi-objective design of MPC (MOD-MPC) approach extends current methods that treat control performance and the computational capacity separately – often with the latter as a fixed constraint – which requires the implementation hardware to be known a priori. The proposed approach focuses on the tuning of structural MPC parameters, namely sampling time and prediction horizon length, to produce a set of optimal choices available to the practitioner. The posed design problem is then analyzed to reveal key properties, including smoothness of the design objectives and parameter bounds, and establish certain validated guarantees. Founded on these properties, necessary and sufficient conditions for an effective and efficient optimizer are presented, leading to a specialized multi-objective optimizer for the MOD-MPC being proposed. Finally, two real-world control problems are used to illustrate the results of the tuning approach and importance of the developed conditions for an effective optimizer of the MOD-MPC problem.
Issue Date: 8-Aug-2016
Date of Acceptance: 18-Jul-2016
URI: http://hdl.handle.net/10044/1/41292
DOI: http://dx.doi.org/10.1016/j.conengprac.2016.07.009
ISSN: 0967-0661
Publisher: Elsevier
Start Page: 1
End Page: 12
Journal / Book Title: Control Engineering Practice
Volume: 56
Copyright Statement: © 2016 Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/
Sponsor/Funder: Commission of the European Communities
Funder's Grant Number: PITN-GA-2013-607957
Keywords: Industrial Engineering & Automation
0102 Applied Mathematics
0906 Electrical And Electronic Engineering
0913 Mechanical Engineering
Publication Status: Published
Appears in Collections:Faculty of Engineering
Electrical and Electronic Engineering



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