Analysis and reduced-order design of quadratic criterion-based iterative learning control using singular value decomposition

Kim, Won Cheol; Chin, In Sik; Lee, Kwang Soon; Choi, Jae Young

doi:10.1016/s0098-1354(00)00503-2

Cited by 27 publications

(12 citation statements)

References 6 publications

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“…As can be seen from eq 3, the novelty of the proposed ILC strategy is that the learning is performed in the reduced dimensional space (latent variable or score space) of a PLS model rather than in the real space of the MVTs. This ILC approach is in principle similar to the low order ILC algorithm presented by Kim et al However, with the LV-ILC proposed in this paper, it is possible to include not only the MVTs ( u c ) but also any other available process measurement (or inferential variable) x m . Inclusion of x m incorporates information on disturbances and slow process condition changes, which allows performing disturbance compensation within a batch (detailed discussion on the similarities and differences of the LV-ILC algorithm with the one presented by Kim et al is given in section 3.7).…”

Section: Control Methodologymentioning

confidence: 99%

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Iterative Learning Control for Final Batch Product Quality Using Partial Least Squares Models

Flores‐Cerrillo

MacGregor

2005

Ind. Eng. Chem. Res.

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A terminal iterative learning control (ILC) strategy for batch-to-batch and within-batch control of final product properties, based on empirical partial least squares (PLS) models, is presented. The strategy rejects persistent process disturbances and achieves new final product quality targets using an iterative procedure that works in the reduced space of a latent variable model rather than in the high dimensional space of the manipulated variable trajectories. Complete manipulated variable trajectory reconstruction is then achieved by exploiting the PLS model of the process. The approach is illustrated with a condensation polymerization example for the production of nylon.

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Section: Control Methodologymentioning

confidence: 99%

“…More recently, the problem of ill-conditioned inversion (that can be seen as a form of a nonsquare system) has been studied. 26 In this work, single value decomposition (SVD) was used to reduce the dimensionality of the system. By doing so, much better trajectory tracking performance was obtained.…”

Section: Introductionmentioning

confidence: 99%

Iterative Learning Control for Final Batch Product Quality Using Partial Least Squares Models

Flores‐Cerrillo

MacGregor

2005

Ind. Eng. Chem. Res.

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“…This extension provided a more convenient and intuitive tuning knob to control the rate of convergence and the ability to take a more systematic account of disturbances and noises. A robustness study of Q-ILC indicated that convergence can be achieved in the presence of a fairly large model error (Kim, Chin, Lee, & Choi, 2000;. They also pointed out that, when the constraints are given as linear inequalities, the optimal input profile respecting the constraints can be obtained through a quadratic programming technique.…”

Section: Model-based Formulationmentioning

confidence: 99%

Iterative learning control applied to batch processes: An overview

Lee

2007

Control Engineering Practice

257

106

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With the recent emphasis on batch processing by emerging industries like the microelectronics and biotechnology, the interest in batch process control has been renewed. This paper gives an overview of the iterative learning control (ILC) technique, which can be used to improve tracking control performance in batch processes. The fundamental concepts and review of the various ILC algorithms are presented, with a particular focus on a model-based algorithm called Q-ILC and an application involving a rapid thermal processing (RTP) system. The study indicates that one can solve a seemingly very difficult multivariable nonlinear tracking problem with relative ease by intelligently combining the ILC technique with basic process insights and standard system identification techniques. Some related techniques in the literature are brought forth with the hope of unifying them. We aslo suggest some remaining challenges. r

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“…Reduced-order ILCs, on the other hand, already appeared in the literature within the framework presented in [10], see e.g. [11], [12] for particular applications. In these approaches, the reduced ILC order results from a reduced plant description using basis functions, commonly determined by the task description (e.g.…”

Section: Introduction Iterative Learning Control (Ilc)mentioning

confidence: 99%

Reduced-Order Iterative Learning Control and a Design Strategy for Optimal Performance Tradeoffs

Pipeleers

Moore

2012

IEEE Trans. Automat. Contr.

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Abstract-When iterative learning control (ILC) is applied to improve a system's tracking performance, the trial-invariant reference input is typically known or contained in a prescribed set of signals. To account for this knowledge, we propose a novel ILC structure that only responds to a given set of trial-invariant inputs. The controllers are called reduced-order ILCs as their order is less than the discrete-time trial length.Exploiting all knowledge available on the input signals is instrumental in facing the fundamental performance limitations in ILC: an ILC is bound to amplify trial-varying inputs and reducing this trial-varying performance degradation invokes a slower learning transient. We present a novel optimal ILC design strategy that allows for a quantitative and systematic analysis of this trade-off. The merit of reduced-order ILCs in view of this tradeoff is demonstrated by numerical results.

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Analysis and reduced-order design of quadratic criterion-based iterative learning control using singular value decomposition

Cited by 27 publications

References 6 publications

Iterative Learning Control for Final Batch Product Quality Using Partial Least Squares Models

Iterative Learning Control for Final Batch Product Quality Using Partial Least Squares Models

Iterative learning control applied to batch processes: An overview

Reduced-Order Iterative Learning Control and a Design Strategy for Optimal Performance Tradeoffs

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