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Computationally efficient infinite-horizon indefinite model predictive control with disturbance preview information

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posted on 2023-01-26, 09:08 authored by Siyuan Zhan, Wen-Hua ChenWen-Hua Chen, Thomas SteffenThomas Steffen, John Ringwood
This paper proposes a model predictive control (MPC) scheme for maximising the benefit of a useful disturbance by exploiting preview information of the disturbance, in the context of goal-oriented operation. For a constrained system, subject to a persistent, bounded, and predictable disturbance, rather than attenuating the influence of disturbance, the proposed MPC aims to utilise the disturbance to optimise high-level economic criteria, e.g., profitability and productivity, which are normally represented by an indefinite cost function. For linear time-invariant systems, after examining the influence of the future disturbance profile, a computationally efficient finite-horizon convex approach is proposed to approximate the solution of the original possibly non-convex infinite-horizon optimisation problem. Then, a receding-horizon implementation is developed, taking into account the recursively updated disturbance prediction, and the recursive feasibility and input-to-state stability of the implementation are established. Numerical examples are provided to verify the efficacy of the proposed method.

Funding

Goal-Oriented Control Systems (GOCS): Disturbance, Uncertainty and Constraints

Engineering and Physical Sciences Research Council

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ViVID - Virtual Vehicle Integration and Development

Innovate UK

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MAREI_Phase 2

Science Foundation Ireland

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History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Aeronautical and Automotive Engineering

Published in

Automatica

Volume

146

Issue

2022

Publisher

Elsevier

Version

  • VoR (Version of Record)

Rights holder

© The Author(s)

Publisher statement

This is an Open Access Article. It is published by Elsevier under the Creative Commons Attribution 4.0 International Licence (CC BY). Full details of this licence are available at: http://creativecommons.org/licenses/by/4.0.

Acceptance date

2022-08-31

Publication date

2022-10-18

Copyright date

2022

ISSN

0005-1098

Language

  • en

Depositor

Prof. Wen-Hua Chen. Deposit date: 25 January 2023

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