Data-driven latent-variable model-based predictive control for continuous processes

Lauri, David; Rossiter, J.A.; Sanchis, Javier; Martínez, M.

doi:10.1016/j.jprocont.2010.08.002

Cited by 40 publications

(18 citation statements)

References 27 publications

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“…LV-MPC for continuous processes is presented in [6]. This Section briefly describes the LV-MPC methodology and reformulates the most relevant formulas in the sake of readability of the present paper.…”

Section: Lv-mpc Methodologymentioning

confidence: 99%

“…This Section briefly describes the LV-MPC methodology and reformulates the most relevant formulas in the sake of readability of the present paper. For further details, the reader is referred to [6].…”

Section: Lv-mpc Methodologymentioning

confidence: 99%

“…LV-MPC is a model-based predictive control methodology implemented in the space of the latent variables. LV-MPC can be applied to batch [5] and continuous [6] processes. The advantages of using LV-MPC include:…”

Section: Introductionmentioning

confidence: 99%

“…In [13] Hotelling's T 2 index is weighted in the MPC cost function to avoid decisions using the model outside its validity range. In [6] Hotelling's T 2 index along with squared residuals in the input space are weighted in the MPC cost function. Weighting such indices in the MPC cost function can avoid the optimization to use the model outside its validity range, but yields the following drawbacks:…”

Section: Introductionmentioning

confidence: 99%

“…• ( The structure of this paper is as follows: The LV-MPC approach for continuous processes in [6] is summed up in Section 2. The validity indicators proposed in this paper are introduced in Section 3.…”

Section: Introductionmentioning

confidence: 99%

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Latent variable based model predictive control: Ensuring validity of predictions

Lauri

Sanchis

Martínez

et al. 2013

Journal of Process Control

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This paper presents a methodology to constrain the optimization problem in LV-MPC so that validity of predictions can be ascertained. LV-MPC is a model-based predictive control methodology implemented in the space of the latent variables and is based on a linear predictor. Provided real processes are non-linear, there is model-process mismatch, and under tight control, the predictor can be used for extrapolation. Extrapolation leads to bad predictions which deteriorates control performance, hence the interest in validity of predictions. In the proposed approach first two validity indicators on predictions are defined. The novelty in the two indicators proposed is they neglect past data, and so validity of predictions is ascertained in terms of future moves which are actually the degrees of freedom in the optimization. Second, the indicators are introduced in the optimization as constraints. Provided the indicators are quadratic, recursive optimization with linearised constraints is implemented. A MIMO example shows how ensuring validity of predictions neglecting past data can improve closed-loop performance, specially under tight control outside the identification region.

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Section: Lv-mpc Methodologymentioning

confidence: 99%

Section: Lv-mpc Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Latent variable based model predictive control: Ensuring validity of predictions

Lauri

Sanchis

Martínez

et al. 2013

Journal of Process Control

Self Cite

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Optimal control applications and methods literature survey (NO. 21–22)

2011

Optim Control Appl Methods

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In order to keep readers up-to-date, the journal will contain a list of recently published journal articles relevant to the fields of optimal control, system optimization and applications six times a year. This list is compiled from a wide range of journals. Please note that even though the list has been broadly categorized these classifications are by no means strict, and are there to assist the reader. Please also note that inclusion in the list is not an endorsement of a paper's quality. If you have any suggestions or comments, please e-mail Leonardo Giovanini at lgiovanini@fich.unl.edu.ar.

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Robust and nonlinear control literature survey (No. 22)

2011

Intl J Robust & Nonlinear

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In order to keep readers up-to-date, the journal will contain a list of recently published journal articles relevant to the fields of robust and nonlinear control six times a year. This list is compiled from a wide range of journals. Please note that although the list has been broadly categorized, these classifications are by no means strict, and are there to assist the reader. Please also note that inclusion in the list is not an endorsement of a paper's quality. If you have any suggestions or comments, please e-mail Dr Jiqiang Wang at jiqiang.wang@nuaa.edu.cn. THEORETICAL DEVELOPMENTS IN ROBUST CONTROL

show abstract

Data-driven latent-variable model-based predictive control for continuous processes

Cited by 40 publications

References 27 publications

Latent variable based model predictive control: Ensuring validity of predictions

Latent variable based model predictive control: Ensuring validity of predictions

Optimal control applications and methods literature survey (NO. 21–22)

Robust and nonlinear control literature survey (No. 22)

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