Data-driven indirect adaptive model predictive control

This paper explores the design of Adaptive Model Predictive Control (AMPC) using Subspace State–space Model Identification (SMI) techniques for an activated sludge process. The implementation of SMI techniques in the adaptive sliding window control methods are discussed where the online subspace ide...

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Main Authors: Wahab, N. A., Katebi, M. R., Rahmat, M. F., Bunyamin, S.
Format: Article
Published: Penerbit UTM Press 2010
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Online Access:http://eprints.utm.my/id/eprint/25921/
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spelling my.utm.259212018-03-22T10:52:18Z http://eprints.utm.my/id/eprint/25921/ Data-driven indirect adaptive model predictive control Wahab, N. A. Katebi, M. R. Rahmat, M. F. Bunyamin, S. TK Electrical engineering. Electronics Nuclear engineering This paper explores the design of Adaptive Model Predictive Control (AMPC) using Subspace State–space Model Identification (SMI) techniques for an activated sludge process. The implementation of SMI techniques in the adaptive sliding window control methods are discussed where the online subspace identification using Numerical State–space Subspace System Identification (N4SID) algorithm is proposed along with Model Predictive Control (MPC) design method. The online N4SID algorithm developed in this study makes use of the QR–updating where the combination of update and down date techniques enables sliding window adaptation. Here, at each time step, for the new experimental data added into R factor, the oldest data are removed. Also, the Singular Value Decomposition (SVD–based) strategy is proposed into Indirect AMPC (IAMPC) for the control increment input constrained nonlinear system. Several simulation studies for different control parameters in control/identification algorithm are performed. For the IAMPC control design, the computational times involved using an SVD approach shows less burdensome compared to Quadratic Programming (QP) method and such an interesting result is considered as one of the main contribution in this paper. Penerbit UTM Press 2010 Article PeerReviewed Wahab, N. A. and Katebi, M. R. and Rahmat, M. F. and Bunyamin, S. (2010) Data-driven indirect adaptive model predictive control. Journal Teknologi, 54 . 141 -163. ISSN 0127–9696 DOI: https://doi.org/10.11113/jt.v54.807 DOI: 10.11113/jt.v54.807
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Wahab, N. A.
Katebi, M. R.
Rahmat, M. F.
Bunyamin, S.
Data-driven indirect adaptive model predictive control
description This paper explores the design of Adaptive Model Predictive Control (AMPC) using Subspace State–space Model Identification (SMI) techniques for an activated sludge process. The implementation of SMI techniques in the adaptive sliding window control methods are discussed where the online subspace identification using Numerical State–space Subspace System Identification (N4SID) algorithm is proposed along with Model Predictive Control (MPC) design method. The online N4SID algorithm developed in this study makes use of the QR–updating where the combination of update and down date techniques enables sliding window adaptation. Here, at each time step, for the new experimental data added into R factor, the oldest data are removed. Also, the Singular Value Decomposition (SVD–based) strategy is proposed into Indirect AMPC (IAMPC) for the control increment input constrained nonlinear system. Several simulation studies for different control parameters in control/identification algorithm are performed. For the IAMPC control design, the computational times involved using an SVD approach shows less burdensome compared to Quadratic Programming (QP) method and such an interesting result is considered as one of the main contribution in this paper.
format Article
author Wahab, N. A.
Katebi, M. R.
Rahmat, M. F.
Bunyamin, S.
author_facet Wahab, N. A.
Katebi, M. R.
Rahmat, M. F.
Bunyamin, S.
author_sort Wahab, N. A.
title Data-driven indirect adaptive model predictive control
title_short Data-driven indirect adaptive model predictive control
title_full Data-driven indirect adaptive model predictive control
title_fullStr Data-driven indirect adaptive model predictive control
title_full_unstemmed Data-driven indirect adaptive model predictive control
title_sort data-driven indirect adaptive model predictive control
publisher Penerbit UTM Press
publishDate 2010
url http://eprints.utm.my/id/eprint/25921/
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score 13.211869