A Frequency-Domain Approach for Enhanced Performance and Task Flexibility in Finite-Time ILC (2403.02039v1)
Abstract: Iterative learning control (ILC) is capable of improving the tracking performance of repetitive control systems by utilizing data from past iterations. The aim of this paper is to achieve both task flexibility, which is often achieved by ILC with basis functions, and the performance of frequency-domain ILC, with an intuitive design procedure. The cost function of norm-optimal ILC is determined that recovers frequency-domain ILC, and consequently, the feedforward signal is parameterized in terms of basis functions and frequency-domain ILC. The resulting method has the performance and design procedure of frequency-domain ILC and the task flexibility of basis functions ILC, and are complimentary to each other. Validation on a benchmark example confirms the capabilities of the framework.
- J. Butterworth, L. Pao, and D. Abramovitch, “Analysis and comparison of three discrete-time feedforward model-inverse control techniques for nonminimum-phase systems,” Mechatronics, vol. 22 (5), 2012.
- M. Boerlage, R. Tousain, and M. Steinbuch, “Jerk derivative feedforward control for motion systems,” in Am. Control Conf., vol. 5 (1), 2004.
- P. Lambrechts, M. Boerlage, and M. Steinbuch, “Trajectory planning and feedforward design for electromechanical motion systems,” Control Eng. Pract., vol. 13 (2), 2005.
- T. Oomen, “Control for Precision Mechatronics,” in Encycl. Syst. Control. London: Springer London, 2020, pp. 1–10.
- D.A. Bristow ; M. Tharayil ; A.G Alleyne., D. A. Bristow, and M. Tharayil, “A survey of iterative learning control,” IEEE Control Syst., vol. 26 (3), 2006.
- S. Arimoto, S. Kawamura, and F. Miyazaki, “Bettering operation of Robots by learning,” J. Robot. Syst., vol. 1 (2), 1984.
- M. Norrlöf and S. Gunnarsson, “Time and frequency domain convergence properties in iterative learning control,” Int. J. Control, vol. 75 (14), 2002.
- S. Gunnarsson and M. Norrlöf, “On the design of ILC algorithms using optimization,” Automatica, vol. 37 (12), 2001.
- M. Phan and J. Frueh, “Learning control for trajectory tracking using basis functions,” in Proc. 35th IEEE Conf. Decis. Control, vol. 3, 1996.
- J. van de Wijdeven and O. Bosgra, “Using basis functions in iterative learning control: analysis and design theory,” Int. J. Control, vol. 83 (4), 2010.
- S. Mishra and M. Tomizuka, “Projection-Based Iterative Learning Control for Wafer Scanner Systems,” IEEE/ASME Trans. Mechatronics, vol. 14 (3), 2009.
- F. Boeren, A. Bareja, T. Kok, and T. Oomen, “Frequency-Domain ILC Approach for Repeating and Varying Tasks: With Application to Semiconductor Bonding Equipment,” IEEE/ASME Trans. Mechatronics, vol. 21 (6), 2016.
- K. Tsurumoto, W. Ohnishi, and T. Koseki, “Task Flexible and High Performance ILC: Preliminary Analysis of Combining a Basis Function and Frequency Domain Design Approach,” in IFAC World Congr., 2023.
- J. X. Xu and Y. Tan, “On the robust optimal design and convergence speed analysis of iterative learning control approaches,” Automatica, vol. 15 (1), 2002.
- B. D. Gorinevsky, “Loop shaping for iterative control of batch processes,” IEEE Control Syst., vol. 22 (6), 2002.
- J. van Zundert, J. Bolder, and T. Oomen, “Optimality and flexibility in Iterative Learning Control for varying tasks,” Automatica, vol. 67, 2016.
- J. Kon, N. de Vos, D. Bruijnen, J. van de Wijdeven, M. Heertjes, and T. Oomen, “Learning for Precision Motion of an Interventional X-ray System: Add-on Physics-Guided Neural Network Feedforward Control,” in 22nd IFAC World Congr., 2023.
- M. Tomizuka, “Zero Phase Error Tracking Algorithm for Digital Control,” ASME. J. Dyn. Sys., Meas., Control, vol. 109 (1), 1987.
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