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Fractional-order Iterative Learning Control with Nonuniform Trial Lengths

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  • Control Theory and Applications
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Abstract

This article addresses a fractional-order iterative learning control (FOILC) design problem for linear time-varying systems with nonuniform trial lengths. A closed-loop FOILC updating law is introduced for tracking tasks with nonuniform trial lengths. To mitigate the influence of nonuniform trail lengths, redefined tracking errors along with current iteration information are adopted to construct control signal. Strict convergence analysis of the tracking error in iteration domain is given. Finally, the efficiency and performance of the proposed approach are verified by three illustrative examples.

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Authors and Affiliations

  1. School of Electrical Engineering and Automation, Qilu University of Technology (Shandong Aacademy of Sciences), Jinan, China

    Yang Zhao & Haiying Liu

  2. School of Control Science and Engineering, Shandong University, Jinan, China

    Yan Li

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  1. Yang Zhao
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Correspondence to Yang Zhao.

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Publisher’s Note

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This work is supported by National Natural Science Foundation of China (Grant No.61773242, 61973193), Intelligent Robot and System Innovation Center Foundation (Grant No.2019IRS19), International cooperation project (Grant No.QLUTGJHZ2018019).

Yang Zhao received her M.S. and Ph.D. degrees from Shandong University, Jinan, China, in 2014 and 2019, respectively. She was an exchange Ph.D. student in the School of Engineering, University of California, Merced, from 2017 to 2018. Since 2019, she has been a Faculty Member of the School of Electrical Engineering and Automation, Qilu University of Technology. Her research interests include iterative learning control, applied fractional calculus in cybernetics, and robotics.

Yan Li received his Ph.D. degree in applied mathematics from Shandong University, Jinan, China, in 2008. He was a Visiting Scholar with CSOIS, Utah State University, from 2007 to 2010. Since 2010, he has been a Faculty Member of the School of Control Science and Engineering, Shandong University. His research interests include applied fractional calculus in cybernetics, test, modeling, and simulation of power batteries, big data analytics in power batteries and microbes, biomechanics, iterative learning control, high gain adaptive control, optimal control, and complex systems and networks.

Haiying Liu received her M.S. and Ph.D. degrees from the School of Control Science and Engineering, Shandong University, in 2007 and 2012, respectively. She was a joint Ph.D. student from August 2009 to August 2011 in the Department of Electrical and Computer Engineering, University of Victoria, Canada. She conducted research as a postdoctoral fellow in the Department of Electrical and Computer Engineering, Dalhousie University, Canada from February 2015 to August 2016. She joined the School of Electrical Engineering and Automation, Qilu University of Technology from 2013 and has been an Associate Professor from 2017. Her current research interests are mainly in the field of pattern recognition, digital image processing and robot vision, and stability analysis with application in mobile robots.

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Zhao, Y., Li, Y. & Liu, H. Fractional-order Iterative Learning Control with Nonuniform Trial Lengths. Int. J. Control Autom. Syst. 20, 3167–3176 (2022). https://doi.org/10.1007/s12555-021-0536-8

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  • DOI: https://doi.org/10.1007/s12555-021-0536-8

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