A multi-objective iterative learning control approach for additive manufacturing applications

Lim, In-Gyu; Hoelzle, David J.; Barton, Kira

doi:10.1016/j.conengprac.2017.03.011

Cited by 34 publications

(18 citation statements)

References 36 publications

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“…A different ILC approach was developed in [53] for addictive manufacturing, where no temporal dynamics was involved, but it is not suitable for spatial path tracking. Please refer to [57], [58] for more information.…”

Section: Piecewise Linear Spatial Path Trackingmentioning

confidence: 99%

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Generalized Iterative Learning Control Using Successive Projection: Algorithm, Convergence, and Experimental Verification

Chen

Chu

Freeman

2020

IEEE Trans. Contr. Syst. Technol.

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Iterative learning control (ILC) is a high performance control design method for systems working in a repetitive manner. ILC has traditionally focused on tracking a reference defined at all points over a finite time interval; recent developments have begun to exploit the design freedom unlocked by tracking only a finite number of distinct time instants driven by the needs of e.g. robotic pick-and-place tasks. This paper proposes a generalized ILC paradigm which extends and unifies the scope of existing design frameworks by amalgamating previous task descriptions and embedding system constraints on the input and output. A novel solution is then derived using a successive projection method which provides well defined convergence properties. The proposed design framework is illustrated by applying it to a spatial reference tracking problem with experimental results on a gantry robot testing platform demonstrating its effectiveness.

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Section: Piecewise Linear Spatial Path Trackingmentioning

confidence: 99%

“…which yields P i a i = 0, and further gives rise to the condition (57). In addition, the hard output constraint set defined in (58) is used to prevent overshoot.…”

Section: Appendix F Proof Of Theoremmentioning

confidence: 99%

Generalized Iterative Learning Control Using Successive Projection: Algorithm, Convergence, and Experimental Verification

Chen

Chu

Freeman

2020

IEEE Trans. Contr. Syst. Technol.

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“…Perfect tracking is obtained if Q = I is feasible, that is, if it satisfies the convergence constraint as given by (4), since for Q = I, (L, I, Δ) = S • = 0. If Q is taken as Q = I in the optimization problems posed by (9) and (10), then only the convergence constraint (4) needs to be satisfied. Hence, (9) and (10) reduce to a feasibility problem in L. If a set of feasible L exists, then a unique solution can be determined by minimizing in (4).…”

Section: Optimizing L For Fast Convergence and Perfect Trackingmentioning

confidence: 99%

Multivariable nonparametric learning: A robust iterative inversion‐based control approach

Rozario

Oomen

2020

Intl J Robust & Nonlinear

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Learning control enables significant performance improvement for systems by utilizing past data. Typical design methods aim to achieve fast convergence by using prior system knowledge in the form of a parametric model. To ensure that the learning process converges in the presence of model uncertainties, it is essential that robustness is appropriately introduced, which is particularly challenging for multivariable systems. The aim of the present article is to develop an optimization-based design framework for fast and robust learning control for multivariable systems. This is achieved by connecting robust control and nonparametric frequency response function identification, which results in a design approach that enables the synthesis of learning and robustness parameters on a frequency-by-frequency basis. Application to a multivariable benchmark motion system confirms the potential of the developed framework. K E Y W O R D S frequency response methods, learning control, multivariable control systems, robust control, System identification 1 INTRODUCTION Learning control methods can increase the performance of systems by using past operational data. Iterative learning control (ILC), 1,2 repetitive control (RC), 3 and iterative inversion-based control (IIC) 4 have been developed to increase the tracking performance of systems that start each task under identical conditions or that operate continuously in a periodic fashion. 5,6 Examples of high-precision applications whose performance has been enhanced through learning control include nanopositioning devices, 7,8 additive manufacturing machines, 9,10 and industrial printing systems. 11,12 Convergence, usually in terms of signal norms, is an essential aspect of learning and is typically subject to the following requirements. 1,13(§5.1.1) (R1) The learning process converges in a way that is free of poor learning transients and converges relatively fast. (R2) The performance upon convergence is improved. These requirements should hold in the presence of uncertain dynamics and unknown disturbances, 14 since learning control is typically employed to increase the performance in the presence of these unmodeled phenomena. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…In this method, the important information about past experiences is utilised to improve the current behaviour. Up to now, there exist a good deal of researches results which are widely applied in trial‐to‐trial control areas [8–11]. In [12], an innovative robust ILC law is designed for the repetitive process setting.…”

Section: Introductionmentioning

confidence: 99%

Observer‐based iterative learning control for linear time‐varying systems using LMI

Wang

Feng

et al. 2020

J. eng.

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A multi-objective iterative learning control approach for additive manufacturing applications

Cited by 34 publications

References 36 publications

Generalized Iterative Learning Control Using Successive Projection: Algorithm, Convergence, and Experimental Verification

Generalized Iterative Learning Control Using Successive Projection: Algorithm, Convergence, and Experimental Verification

Multivariable nonparametric learning: A robust iterative inversion‐based control approach

Observer‐based iterative learning control for linear time‐varying systems using LMI

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