Using iterative learning control with basis functions to compensate medium deformation in a wide-format inkjet printer

Bolder, JJ Joost; Oomen, Tom; Koekebakker, Sjirk; Steinbuch, M Maarten

doi:10.1016/j.mechatronics.2014.07.003

Cited by 67 publications

(68 citation statements)

References 30 publications

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“…Subjects of ongoing research include: signal conditioning in the experiments, using basis functions for increased convergence speed and robustness against measurement noise [5], reducing the number of experiments needed in the data-driven approach, and an automated method for the selection of ε using the approach in [30].…”

Section: Conclusion and Ongoing Researchmentioning

confidence: 99%

“…After each repetition the command signal for the next repetition is updated by learning from past executions. Examples applications include: additive manufacturing machines [2], [3], robotic arms [4], printing systems [5], pick and place machines, electron microscopes, and wafer stages [6]- [8].…”

Section: Introductionmentioning

confidence: 99%

“…This approach is used with ILC algorithm (5), to arrive at the data-driven ILC algorithm for MIMO systems.…”

mentioning

confidence: 99%

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Data-driven optimal ILC for multivariable systems: Removing the need for L and Q filter design

Bolder

Oomen

2015

2015 American Control Conference (ACC)

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Abstract-Many iterative learning control algorithms rely on a model of the system. Although only approximate model knowledge is required, the model quality determines the convergence and performance properties of the learning control algorithm. The aim of this paper is to remove the need for a model for a class of multivariable ILC algorithms. The main idea is to replace the model by dedicated experiments on the system. Convergence criteria are developed and the results are illustrated with a simulation on a multi-axis flatbed printer.

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Section: Conclusion and Ongoing Researchmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Data-driven optimal ILC for multivariable systems: Removing the need for L and Q filter design

Bolder

Oomen

2015

2015 American Control Conference (ACC)

Self Cite

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“…ILC therefore particularly works well for repetitive control systems, which have the same reference and perform the same task over and over again. ILC has been successfully applied to for example industrial motion systems [7,8]. The use of ILC in tokamaks was proposed in [9], where its application to current density profile control is discussed.…”

Section: Introductionmentioning

confidence: 99%

Density control in ITER: an iterative learning control and robust control approach

et al. 2017

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Plasma density control for next generation tokamaks, such as ITER, is challenging because of multiple reasons. The response of the usual gas valve actuators in future, larger fusion devices, might be too slow for feedback control. Both pellet fuelling and the use of feedforward-based control may help to solve this problem. Also, tight density limits arise during ramp-up, due to operational limits related to divertor detachment and radiative collapses. As the number of shots available for controller tuning will be limited in ITER, in this paper, iterative learning control (ILC) is proposed to determine optimal feedforward actuator inputs based on tracking errors, obtained in previous shots. This control method can take the actuator and density limits into account and can deal with large actuator delays. However, a purely feedforward-based density control may not be sufficient due to the presence of disturbances and shot-to-shot differences. Therefore, robust control synthesis is used to construct a robustly stabilizing feedback controller. In simulations, it is shown that this combined controller strategy is able to achieve good tracking performance in the presence of shot-to-shot differences, tight constraints, and model mismatches.

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“…From (20), the definition of ω in (15), which implies that there exists a constant L ω2 ∈ R >0 such that ω ≤ L ω2 , and the bound in (165) we obtain…”

Section: Proof Of Lemmamentioning

confidence: 99%

Extremum-seeking control for steady-state performance optimization of nonlinear plants with time-varying steady-state outputs

Hazeleger

Haring

Wouw

2018

2018 Annual American Control Conference (ACC)

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Using iterative learning control with basis functions to compensate medium deformation in a wide-format inkjet printer

Cited by 67 publications

References 30 publications

Data-driven optimal ILC for multivariable systems: Removing the need for L and Q filter design

Data-driven optimal ILC for multivariable systems: Removing the need for L and Q filter design

Density control in ITER: an iterative learning control and robust control approach

Extremum-seeking control for steady-state performance optimization of nonlinear plants with time-varying steady-state outputs

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