On adaptive optimal input design: A bioreactor case study

Stigter, J.D.; Vries, Diemer de; Keesman, Karel J.

doi:10.1002/aic.10923

Cited by 45 publications

(33 citation statements)

References 11 publications

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“…Stigter et al 8 proposed an adaptive approach for experiment design with a receding-horizon idea. Jayasankar et al 16 later formalized the design and called it as a receding-horizon experiment design.…”

Section: Receding-horizon Designmentioning

confidence: 99%

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Constrained receding‐horizon experiment design and parameter estimation in the presence of poor initial conditions

Zhu

Huang

2010

AIChE Journal

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An optimal experiment design assumes the existence of an initial or nominal process model. The efficiency of this procedure depends on how the initial model is chosen. This creates a practical dilemma as estimating the model is precisely what the experiment tries to achieve. A novel approach to experiment design for identification of nonlinear systems is developed, with the purpose of reducing the influence of poor initial values. The experiment design and the parameter estimation are conducted iteratively under a receding-horizon framework. By taking steady-state prior knowledge into account, constraints on the parameters can be derived. Such constraints help reduce influence of poor initial models. The proposed algorithm is illustrated through examples to demonstrate its efficiency. V V C 2010 American Institute of Chemical Engineers AIChE J, 57: [2808][2809][2810][2811][2812][2813][2814][2815][2816][2817][2818][2819][2820] 2011 Keywords: receding-horizon design, optimal experiment design, constrained EKF IntroductionOptimal experiment design aims at determining optimal experiment conditions to achieve a specific set of objectives. Experiment design is a broad subject that includes aspects such as input design, operating point design, and sampling time design. Although a significant amount of literature on optimal experiment design for linear systems has been published since the 1970s, 1,2 the optimal experiment design concerning nonlinear systems has remained largely unexplored.One significant challenge for nonlinear experiment design as well as parameters identification is that the sensitivity functions used to search for optimal conditions depend on the unknown model parameters. Three existing methods have been proposed to address this challenge: minimax experiment design, ED (expectation of determinant)/EID (expectation of the inverse of determinant)-optimal design, and adaptive experiment design.Minimax experiment design attempts to achieve robust experiment by minimizing the largest possible modeling error. This approach needs no prior information about parameter distributions. There are two recent representative publications on this topic. Rojas et al.3 developed a method of optimizing the worst case of modeling error over the parameter set, while a convex optimization algorithm is implemented on a linear system. In conjunction, Welsh and Rojas 4 proposed an algorithm to solve a robust optimal experiment design problem by scenario approach. To construct convex or semidefinite convex problems, both techniques formulate the identification problem in frequency domain, but neither of the methods can be used for identification in nonlinear system. Moreover, the optimality objective function for nonlinear identification is generally difficult to be formulated as a convex or semidefinite convex problem.Another group of methods (especially popular in bioscience fields) are experiment designs by optimizing over the expected determinant of a Fisher information matrix Correspondence concerning this article ...

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Section: Receding-horizon Designmentioning

confidence: 99%

“…8 The adaptive design starts from a nominal model guessĥ 0 , by which the design criterion f ðMðĥ 0 ; uÞÞ is optimized. Then, the experiment is conducted for the next one or several samples and the model parameter is identified.…”

Section: Introductionmentioning

confidence: 99%

Constrained receding‐horizon experiment design and parameter estimation in the presence of poor initial conditions

Zhu

Huang

2010

AIChE Journal

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“…This spectrum redesign procedure, inspired by the receding horizon mechanism in MPC control (Maciejowski & Huzmezan, 1997), will be performed after each interval. Note that a similar mechanism for the adaptive solution of an ED problem was adopted in Stigter, Vries, and Keesman (2006).…”

Section: Receding Horizonmentioning

confidence: 99%

Data-driven model improvement for model-based control

Forgione

Bombois²,

Hof

2015

Automatica

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Forgione, M.; Bombois, X.; Van den Hof, P.M.J. Published in: Automatica DOI:10.1016/j.automatica. 2014.11.006 Published: 01/01/2015 Document VersionPublisher's PDF, also known as Version of Record (includes final page, issue and volume numbers) Please check the document version of this publication:• A submitted manuscript is the author's version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website.• The final author version and the galley proof are versions of the publication after peer review.• The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publicationCitation for published version (APA): Forgione, M., Bombois, X., & Hof, Van den, P. M. J. (2015). Data-driven model improvement for model-based control. Automatica, 52, 118-124. DOI: 10.1016/j.automatica.2014 General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.• You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal ? Take down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. We present a framework for the gradual improvement of model-based controllers. The total time of the learning procedure is divided into a number of learning intervals. After a learning interval, the model is refined based on the measured data. This model is used to synthesize the controller that will be applied during the next learning interval. Excitation signals can be injected into the control loop during each of the learning intervals. On the one hand, the introduction of an excitation signal worsens the control performance during the current learning interval since it acts as a disturbance. On the other hand, the informative data generated owing to the excitation signal are used to refine the model using a closedloop system identification technique. Therefore, the control performance for the next learning interval is expected to improve. In principle, our objective is to maximize the overall control performance taking the effect of the excitation signals explicitly into account. However, this is in general an intractable optimization problem. For this reason, a convex approximation of the original problem is derived using standard relax...

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“…Im Gegensatz zum klassischen, sequenziellen Ansatz, werden hier neue Messdaten bereits direkt nach ihrer Entstehung, parallel zum laufenden Experiment, für die Aktualisierung von Parameterwerten und Versuchsplänen ausgewertet. Vielversprechend ist die Nutzung bereits etablierter Konzepte der modellgestützten Regelung und Zustandsschätzung auf bewegten Horizonten, wie in [18,20,21] …”

Section: Introductionunclassified

Calibration of Dynamic Models through Adaptive Optimal Input Designs

Barz

Wozny

2014

Chemie Ingenieur Technik

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Calibration of Dynamic Models through Adaptive Optimal Input DesignsThe optimum experimental design is a methodical approach to calibrate and validate models with experimental data. It shows great potential towards reduction of experimental efforts and maximizing model accuracy. This article discusses the recent developments in the optimization of the method adaptivity leading to a broader application. Besides concrete examples, the experimental use of the automatic real-time calibration method is presented for a chromatography model.

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On adaptive optimal input design: A bioreactor case study

Cited by 45 publications

References 11 publications

Constrained receding‐horizon experiment design and parameter estimation in the presence of poor initial conditions

Constrained receding‐horizon experiment design and parameter estimation in the presence of poor initial conditions

Data-driven model improvement for model-based control

Calibration of Dynamic Models through Adaptive Optimal Input Designs

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