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Marcel Heertjes, Jazmin Zenteno Torres & Mohammad Al Janaideh. (2023) Fourth-Order Reference Trajectories in Lithography Stages with Weakly-Damped Modes - a Frequency-Domain Perspective. Fourth-Order Reference Trajectories in Lithography Stages with Weakly-Damped Modes - a Frequency-Domain Perspective.
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Liangliang Yang & Hui Zhang. (2022) Data-Driven Feedforward Parameter Tuning Optimization Method Under Actuator Constraints. IEEE/ASME Transactions on Mechatronics 27:5, pages 3429-3439.
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Weike Liu, Zidong Liu, Yang Zhang, Feng Shu & Xiaofeng Yang. (2022) Iterative Feedforward Tuning for A PMLSM with Unknown Time Delay and Current-Dependent Force Ripple. Iterative Feedforward Tuning for A PMLSM with Unknown Time Delay and Current-Dependent Force Ripple.
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Ran Zhou, Chuxiong Hu, Bingyang Hou & Yu Zhu. (2022) Comparative Study of Performance-Oriented Feedforward Compensation Strategies for Precision Mechatronic Motion Systems. IEEE Access 10, pages 100812-100823.
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Fazhi Song, Yang Liu & Qiao Zhu. (2021) Feedforward-Control-Oriented Identification: A Robust Iterative Learning Approach. Feedforward-Control-Oriented Identification: A Robust Iterative Learning Approach.
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Yixiu Sun, Xiaoqing Li, Ying Luo, Xuedong Chen & Lizhan Zeng. (2020) Iterative Tuning of Feedforward Controller with Precise Time-Delay Compensation for Precision Motion System. Mathematical Problems in Engineering 2020, pages 1-23.
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Weicai Huang, Kaiming Yang, Yu Zhu, Xin Li, Haihua Mu & Min Li. (2019) Data-driven rational feedforward tuning: With application to an ultraprecision wafer stage. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 234:6, pages 748-758.
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Li Li, Yang Liu, Liyi Li & Jiubin Tan. (2019) Kalman-Filtering-Based Iterative Feedforward Tuning in Presence of Stochastic Noise: With Application to a Wafer Stage. IEEE Transactions on Industrial Informatics 15:11, pages 5816-5826.
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Qi Liu, Juliang Xiao, Xu Yang, Haitao Liu, Tian Huang & D.G. Chetwynd. (2019) An iterative tuning approach for feedforward control of parallel manipulators by considering joint couplings. Mechanism and Machine Theory 140, pages 159-169.
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Lennart Blanken, Goksan Isil, Sjirk Koekebakker & Tom Oomen. (2018) Data-Driven Feedforward Learning using Non-Causal Rational Basis Functions: Application to an Industrial Flatbed Printer. Data-Driven Feedforward Learning using Non-Causal Rational Basis Functions: Application to an Industrial Flatbed Printer.
Zhao Feng, Jie Ling, Min Ming & Xiaohui Xiao. (2017) A model-data integrated iterative learning controller for flexible tracking with application to a piezo nanopositioner. Transactions of the Institute of Measurement and Control 40:10, pages 3201-3210.
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Frank Boeren, Lennart Blanken, Dennis Bruijnen & Tom Oomen. (2017) Optimal Estimation of Rational Feedforward Control via Instrumental Variables: With Application to a Wafer Stage. Asian Journal of Control 20:3, pages 975-992.
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Lennart Blanken, Ids van den Meijdenberg & Tom Oomen. (2018) Kernel-based regression of non-causal systems for inverse model feedforward estimation. Kernel-based regression of non-causal systems for inverse model feedforward estimation.
Tom Oomen. (2018) Advanced Motion Control for Precision Mechatronics: Control, Identification, and Learning of Complex Systems. IEEJ Journal of Industry Applications 7:2, pages 127-140.
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M. Brunot & A. Janot. (2018) A New Recursive Instrumental Variables Approach for Robot Identification. IFAC-PapersOnLine 51:15, pages 132-137.
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Tom Oomen & Cristian R. Rojas. (2017) Sparse iterative learning control with application to a wafer stage: Achieving performance, resource efficiency, and task flexibility. Mechatronics 47, pages 134-147.
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Zhao Feng, Jie Ling, Min Ming & Xiao-Hui Xiao. (2017) High-bandwidth and flexible tracking control for precision motion with application to a piezo nanopositioner. Review of Scientific Instruments 88:8.
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Jurgen van Zundert & Tom Oomen. (2017) On Optimal Feedforward and ILC: The Role of Feedback for Optimal Performance and Inferential Control * *This research is supported by the Dutch Technology Foundation STW, carried out as part of the Robust Cyber-Physical Systems (RCPS) project (no. 12694); and Innovational Research Incentives Scheme under the VENI grant "Precision Motion: Beyond the Nanometer" (no. 13073) awarded by NWO (The Netherlands Organization for scientific Research).. IFAC-PapersOnLine 50:1, pages 6093-6098.
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