Design Techniques for Multivariable ILC: Application to an Industrial Flatbed Printer

Blanken, Lennart; Willems, JM Jeroen; Koekebakker, Sjirk; Oomen, Tom

doi:10.1016/j.ifacol.2016.10.552

Cited by 14 publications

(16 citation statements)

References 24 publications

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“…The effectiveness of the framework is demonstrated in a case study on an industrial flatbed printer. The paper extends preliminary results in [23], [24] through the design framework, new technical results, detailed proofs, and application results.…”

Section: Introductionsupporting

confidence: 57%

Multivariable Iterative Learning Control Design Procedures: From Decentralized to Centralized, Illustrated on an Industrial Printer

Blanken

Oomen

2020

IEEE Trans. Contr. Syst. Technol.

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Iterative Learning Control (ILC) enables high control performance through learning from measured data, using only limited model knowledge in the form of a nominal parametric model to guarantee convergence. The aim of this paper is to develop a range of approaches for multivariable ILC, where specific attention is given to addressing interaction. The proposed methods either address the interaction in the nominal model, or as uncertainty, i.e., through robust stability. The result is a range of techniques, including the use of the structured singular value (SSV) and Gershgorin bounds, that provide a different trade-off between modeling requirements, i.e., modeling effort and cost, and achievable performance. This allows an appropriate choice in view of modeling budget and performance requirements. The trade-off is demonstrated in a case study on an industrial printer.

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Section: Introductionsupporting

confidence: 57%

Multivariable Iterative Learning Control Design Procedures: From Decentralized to Centralized, Illustrated on an Industrial Printer

Blanken

Oomen

2020

IEEE Trans. Contr. Syst. Technol.

Self Cite

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“…For NPZIgnore, ZPETC, and ZMETC the extension to multivariable systems is nontrivial. In [20], an approach for multivariable ZPETC is proposed which applies multiple times SISO ZPETC to the Smith form of the system. Similarly, the Smith form can be used to construct NPZIgnore and ZMETC.…”

Section: Extensions: Multivariable Time Varying Parameter Varying mentioning

confidence: 99%

“…Similarly, the Smith form can be used to construct NPZIgnore and ZMETC. However, in [20] it is concluded that at present there are no numerically stable algorithms for finding Smith forms. Therefore, these techniques currently seem to be limited to SISO systems.…”

Section: Extensions: Multivariable Time Varying Parameter Varying mentioning

confidence: 99%

“…Alternatively, infinite preview [19] or H ∞ based [11,12] techniques can be used. However, [11,12] lack the use of preview, and are recently extended in [20] towards preview/fixed lag smoothing situations. This paper provides guidelines on proper use of inversion techniques for both inverse model feedforward and learning control by addressing the application specific objective.…”

Section: Introductionmentioning

confidence: 99%

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On inversion-based approaches for feedforward and ILC

2018

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“…First, for NPZ-Ignore, ZPETC, and ZMETC the extension to multivariable systems is nontrivial. In [66,30], an approach for multivariable ZPETC is proposed which applies multiple times SISO ZPETC to the Smith form of the system. Similarly, the Smith form can be used to construct MIMO inverses using NPZ-Ignore and ZMETC.…”

Section: Design Of Learning Filter: Mimo Inversion Techniquesmentioning

confidence: 99%

Multivariable iterative learning control: analysis and designs for engineering applications

Blanken

Zundert

Rozario

et al. 2019

Data-Driven Modeling, Filtering and Control: Methods and Applications

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Iterative Learning Control (ILC) enables high control performance through learning from measured data, using limited model knowledge, typically in the form of a nominal parametric model. Robust stability requires robustness to modeling errors, often due to deliberate undermodeling. The aim of this chapter is to outline a range of design approaches for multivariable ILC that is suited for engineering applications, with specific attention to addressing interaction using limited model knowledge. The proposed methods either address the interaction in the nominal model, or as uncertainty, i.e., through robust stability. The result is a range of techniques, including the use of the structured singular value (SSV) and Gershgorin bounds, that provide a different trade-off between modeling requirements, i.e., modeling effort and cost, and achievable performance. This allows control engineers to select the approach that fits best the modeling budget and control requirements. This trade-off is demonstrated in case studies on industrial printers. Additionally, two learning approaches are presented that are compatible with, and provide extensions to, the developed multivariable design framework: model-free iterative learning, and ILC for varying tasks.

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Design Techniques for Multivariable ILC: Application to an Industrial Flatbed Printer

Cited by 14 publications

References 24 publications

Multivariable Iterative Learning Control Design Procedures: From Decentralized to Centralized, Illustrated on an Industrial Printer

Multivariable Iterative Learning Control Design Procedures: From Decentralized to Centralized, Illustrated on an Industrial Printer

On inversion-based approaches for feedforward and ILC

Multivariable iterative learning control: analysis and designs for engineering applications

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