Iterative Learning of Optimal Control for Nonlinear Processes With Applications to Laser Additive Manufacturing

Rafajłowicz, Ewaryst; Jurewicz, Piotr; Reiner, Jacek

doi:10.1109/tcst.2018.2865444

Cited by 14 publications

(6 citation statements)

References 29 publications

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“…It was selected because of its well-defined and almost constant reflectance value of over 97.5% for 575 nm [18]. Materials that were compared included a substrate made of mild steel S420MC and a laser with a deposited coating made of stainless steel 316L (S420MC_316L) [19]. The considered issue relates to a common case when steel characterized by sensitivity to corrosion and atmospheric conditions is cladded with a layer of stainless steel that protects the exposed surface exposed from degradation.…”

Section: Samples Preparationmentioning

confidence: 99%

Imaging Scatterometry with Extrapolation of Missing BRDF Data for Materials Used in Laser Material Processing

Zakrzewski

Jurewicz

Ćwikła

et al. 2020

Sensors

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Imaging scatterometry is a method for determining the reflection distribution based on bidirectional reflectance distribution function (BRDF) measurements. However, it has a well-known limitation that results obtained by imaging scatterometry for small illumination angles are practically useless. Therefore, we propose an approach for reconstruction of the reflection distribution based on a series of measurements at different illumination angles and extrapolation of the missing results to overcome this limitation. The developed algorithm was validated using bidirectional transmittance distribution function (BTDF) measurements. The BRDF measurements were carried out for materials that are commonly used in laser material processing, i.e. substrates and functional coatings. The obtained data were subsequently used to determine the total reflection intensity from all considered materials, which were characterized by reconstructed distributions.

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Section: Samples Preparationmentioning

confidence: 99%

Imaging Scatterometry with Extrapolation of Missing BRDF Data for Materials Used in Laser Material Processing

Zakrzewski

Jurewicz

Ćwikła

et al. 2020

Sensors

Self Cite

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“…One possible starting point for the literature is the survey papers [2], [3] and the latest monograph [4]. Recent examples include various forms of additive manufacturing processes, e.g., [5], [6], nanopositioning, e.g., [7], path following for center-articulated industrial vehicles [8]. In the general area of healthcare, ILC has found applications in, e.g., robotic-assisted upper limb stroke rehabilitation with clinical trials, e.g.…”

Section: Introductionmentioning

confidence: 99%

Constrained Iterative Learning Control for Linear Time-Varying Systems With Experimental Validation on a High-Speed Rack Feeder

Chu

Rauh

Aschemann

et al. 2022

IEEE Trans. Contr. Syst. Technol.

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Iterative learning control (ILC) applies to systems required to track the desired trajectory of finite duration repeatedly. This paper considers constrained ILC design for linear time-varying systems, a problem with limited, in relative terms, results in the literature but not uncommon in practical applications. Different design algorithms are developed and their convergence properties established. An extension of these designs to point-to-point tracking tasks is given. A high-speed rack feeder typically used in automated warehouses is considered to verify the designs. It represents a flexible beam structure subject to kinematic constraints such as a maximum velocity and a maximum acceleration with a vertically moving mass causing the time-varying characteristics. Experimental results demonstrate the effectiveness of the designs.

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“…Compared to real-time-feedback and/or feedforward control techniques, many case studies of ILC have shown a substantial reduction in tracking error. Relevant applications include robotassisted stroke rehabilitation 1 , high speed train control 2 , laser additive manufacturing 3 , and vehicle-mounted manipulators 4 , all of which use nonlinear models. In fact, while the majority of ILC literature focuses on linear systems, the prevalence of nonlinear dynamics in real-world systems has motivated the development of numerous ILC theories for discrete-time nonlinear models [5][6][7][8][9][10] .…”

Section: Introductionmentioning

confidence: 99%

Iterative learning control with discrete‐time nonlinear nonminimum phase models via stable inversion

Spiegel

Strijbosch

Oomen

et al. 2021

Intl J Robust & Nonlinear

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Output reference tracking can be improved by iteratively learning from past data to inform the design of feedforward control inputs for subsequent tracking attempts. This process is called iterative learning control (ILC). This article develops a method to apply ILC to systems with nonlinear discrete-time dynamical models with unstable inverses (i.e. discrete-time nonlinear non-minimum phase models). This class of systems includes piezoactuators, electric power converters, and manipulators with flexible links, which may be found in nanopositioning stages, rolling mills, and robotic arms, respectively. As these devices may be required to execute fine transient reference tracking tasks repetitively in contexts such as manufacturing, they may benefit from ILC. Specifically, this article facilitates ILC of such systems by presenting a new ILC synthesis framework that allows combination of the principles of Newton's root finding algorithm with stable inversion, a technique for generating stable trajectories from unstable models. The new framework, called Invert-Linearize ILC (ILILC), is validated in simulation on a cart-and-pendulum system with model error, process noise, and measurement noise. Where preexisting Newton-based ILC diverges, ILILC with stable inversion converges, and does so in less than one third the number of trials necessary for the convergence of a gradient-descent-based ILC technique used as a benchmark.

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Iterative Learning of Optimal Control for Nonlinear Processes With Applications to Laser Additive Manufacturing

Cited by 14 publications

References 29 publications

Imaging Scatterometry with Extrapolation of Missing BRDF Data for Materials Used in Laser Material Processing

Imaging Scatterometry with Extrapolation of Missing BRDF Data for Materials Used in Laser Material Processing

Constrained Iterative Learning Control for Linear Time-Varying Systems With Experimental Validation on a High-Speed Rack Feeder

Iterative learning control with discrete‐time nonlinear nonminimum phase models via stable inversion

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