Disturbance-Kalman state for linear offset free MPC

In model predictive control (MPC), methods of linear offset free MPC are well established such as the disturbance model, the observer method and the state disturbance observer method. However, the observer gain in those methods is difficult to define. Based on the drawbacks observed in those methods...

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Main Authors: Tuan, T.T., Zabiri, H., Mutalib, M.I.A., VO, D.-V.N.
Format: Article
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.33211 /
Published: Polska Akademia Nauk 2022
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130290108&doi=10.24425%2facs.2022.140869&partnerID=40&md5=d55940b2ab86eccbcfd2d41d9822d878
http://eprints.utp.edu.my/33211/
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spelling utp-eprints.332112022-07-06T08:21:06Z Disturbance-Kalman state for linear offset free MPC Tuan, T.T. Zabiri, H. Mutalib, M.I.A. VO, D.-V.N. In model predictive control (MPC), methods of linear offset free MPC are well established such as the disturbance model, the observer method and the state disturbance observer method. However, the observer gain in those methods is difficult to define. Based on the drawbacks observed in those methods, a novel algorithm is proposed to guarantee offset-free MPC under model-plant mismatches and disturbances by combining the two proposed methods which are the proposed Recursive Kalman estimated state method and the proposed Disturbance-Kalman state method. A comparison is made from existing methods to assess the ability of providing offset-free MPC onWood-Berry distillation column. Results shows that the proposed offset free MPC algorithm has better disturbance rejection performance than the existing algorithms. © 2022. The Author(s). Polska Akademia Nauk 2022 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130290108&doi=10.24425%2facs.2022.140869&partnerID=40&md5=d55940b2ab86eccbcfd2d41d9822d878 Tuan, T.T. and Zabiri, H. and Mutalib, M.I.A. and VO, D.-V.N. (2022) Disturbance-Kalman state for linear offset free MPC. Archives of Control Sciences, 32 (1). pp. 153-173. http://eprints.utp.edu.my/33211/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description In model predictive control (MPC), methods of linear offset free MPC are well established such as the disturbance model, the observer method and the state disturbance observer method. However, the observer gain in those methods is difficult to define. Based on the drawbacks observed in those methods, a novel algorithm is proposed to guarantee offset-free MPC under model-plant mismatches and disturbances by combining the two proposed methods which are the proposed Recursive Kalman estimated state method and the proposed Disturbance-Kalman state method. A comparison is made from existing methods to assess the ability of providing offset-free MPC onWood-Berry distillation column. Results shows that the proposed offset free MPC algorithm has better disturbance rejection performance than the existing algorithms. © 2022. The Author(s).
format Article
author Tuan, T.T.
Zabiri, H.
Mutalib, M.I.A.
VO, D.-V.N.
spellingShingle Tuan, T.T.
Zabiri, H.
Mutalib, M.I.A.
VO, D.-V.N.
Disturbance-Kalman state for linear offset free MPC
author_sort Tuan, T.T.
title Disturbance-Kalman state for linear offset free MPC
title_short Disturbance-Kalman state for linear offset free MPC
title_full Disturbance-Kalman state for linear offset free MPC
title_fullStr Disturbance-Kalman state for linear offset free MPC
title_full_unstemmed Disturbance-Kalman state for linear offset free MPC
title_sort disturbance-kalman state for linear offset free mpc
publisher Polska Akademia Nauk
publishDate 2022
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130290108&doi=10.24425%2facs.2022.140869&partnerID=40&md5=d55940b2ab86eccbcfd2d41d9822d878
http://eprints.utp.edu.my/33211/
_version_ 1741197819034730496
score 11.62408

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