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Steady State Frequency Response Design of Finite Time Iterative Learning Control

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Abstract

Steady state frequency response is a major design approach in control theory, and is normally used to obtain reasonable response to commands up to a chosen or obtainable bandwidth. By contrast, iterative learning control (ILC) aims to converge to zero error at all time steps of a finite time tracking task, using an iterative update of the command to a control system. It asks for zero error during the transient as well as steady state response phases. This paper develops a rigorous design approach to apply frequency response to the ILC problem. The gains in an FIR compensator from repetitive control are used to fill the ILC learning gain matrix. The compensator mimics the inverse frequency response. This paper does not ask for zero error for one or more initial times steps, in oder to address a well posed problem that is not trying to converge to an unstable command input. It is seen that by adjusting just one gain in the upper left corner of the learning matrix, the ILC can be stabilized in spite of the length of time involving transients. This makes a very simple design process. One can also adjust two gains in the first row, or better a two-by-two block, but further adjustment seems of limited benefit. The frequency based ILC design developed here is compared to existing ILC design methods, and shown to be vastly superior in convergence rate. It also has the advantage that the ILC can be designed directly from frequency response test data without creating a mathematical model.

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  • 05 June 2020

    In the original article, the sentence below the eq. (13) had an incorrect “a” value.

References

  1. Arimoto, S., Kawamura, S., Miyazaki, F.: Bettering operation of robots by learning. J. Robot. Syst. 1(2), 123–140 (1984)

    Article  Google Scholar 

  2. Bien, Z., Xu, J.-X. (eds.): Iterative learning control: analysis, design, integration and applications. Kluwer Academic Publishers, Boston (1998)

    Google Scholar 

  3. Ahn, H.-S., Chen, Y., Moore, K.L.: Iterative learning control: brief survey and categorization. IEEE Trans. Syst. Man Cybern. Part-C. 37, 1099–1122 (2007)

    Article  Google Scholar 

  4. Edwards, S.G., Agrawal, B.N., Phan, M.Q., Longman, R.W.: Disturbance identification and rejection experiments on an ultra quiet platform. Adv. Astronaut. Sci. 103, 633–651 (1999)

    Google Scholar 

  5. Åström, K., Hagander, P., Strenby, J.: Zeros of sampled systems. In: Proceedings of the Nineteenth IEEE Conference on Decision and Control, pp. 1077–1081. (1980)

  6. Inoue, T., Nakano, M., Iwai, S.: High accuracy control of a proton synchrotron magnet power supply. Proceedings of the 8th World Congress of IFAC, pp. 216–221. (1981)

  7. Middleton, R.H., Goodwin, G.C., Longman, R.W.: A method for improving the dynamic accuracy of a robot performing a repetitive task. Int. J. Robot. Res. 8, 67–74 (1989). Also, University of Newcastle, Newcastle, Australia, Department of Electrical Engineering Technical Report EE8546, 1985

    Article  Google Scholar 

  8. Tomizuka, M., Tsao, T.-C., Chew, K.-K.: Analysis and synthesis of discrete time repetitive controllers. J. Dyn. Syst. Measur. Control. 111, 353–358 (1989)

    Article  Google Scholar 

  9. Longman, R.W.: On the theory and design of linear repetitive control systems. Eur. J. Control. Special Section on Iterative Learning Control, Guest Editor Hyo-Sung Ahn. 16(5), 447–496 (2010)

    Article  MathSciNet  Google Scholar 

  10. Longman, R.W.: Iterative learning control and repetitive control for engineering practice. Int. J. Control. Special Issue on Iterative Learning Control. 73(10), 930–954 (2000)

    Article  MathSciNet  Google Scholar 

  11. Panomruttanarug, B., Longman, R. W.: Repetitive controller design using optimization in the frequency domain. In: Proceedings of the 2004 AIAA/AAS Astrodynamics Specialist Conference, Providence, RI, August 2004

  12. Panomruttanarug, B., Longman, R.W.: Designing optimized FIR repetitive controllers from noisy frequency response data. Adv. Astronaut. Sci. 127, 1723–1742 (2007)

    Google Scholar 

  13. Panomruttanarug, B., Longman, R.W.: Converting repetitive control into stable learning control by iterative adjustment of initial state. Adv. Astronaut. Sci. 124, 667–686 (2006)

    Google Scholar 

  14. Panomruttanarug, B., Longman, R.W., Phan, M.Q.: Designing stable iterative learning control systems from frequency response based repetitive control designs. Adv. Astronaut. Sci. 142, 2893–2912 (2012)

    Google Scholar 

  15. Phan, M., Longman, R.W.: “A mathematical theory of learning control for linear discrete multivariable systems. In: Proceedings of the AIAA/AAS Astrodynamics Conference, Minneapolis, Minnesota, August 1988, pp. 740–746

  16. Longman, R.W., Chang, C.-K., Phan, M.: Discrete time learning control in nonlinear systems. In: A Collection of Technical Papers, 1992 AIAA/AAS Astrodynamics Specialist Conference. Hilton Head, South Carolina, August 1992, pp. 501–511

  17. LeVoci, P.A., Longman, R.W.: Intersample error in discrete time learning and repetitive control. In: Proceedings of the 2004 AIAA/AAS Astrodynamics Specialist Conference. Providence, RI, August 2004

  18. Li, Y., Longman, R.W.: Addressing problems of instability in intersample error in iterative learning control. Adv. Astronaut. Sci. 129, 1571–1591 (2008)

    Google Scholar 

  19. Ji, X., Li, T., Longman, R.W.: Proof of two stable inverses of discrete time systems. Adv. Astronaut. Sci. 162, 123–136 (2018)

    Google Scholar 

  20. Ji, X., Longman, R.W.: The insensitivity of the iterative learning control inverse problem to initial run when stabilized by a new stable inverse. In: Modeling, Simulation and Optimization of Complex Processes HPSC 2018, Springer International Publishing

  21. Elci, H., Longman, R.W., Phan, M.Q., Juang, J.-N., Ugoletti, R.: Simple learning control made practical by zero-phase filtering: applications to robotics. IEEE Trans. Circ. Syst. I: Fund. Theory Appl., Special Issue on Multidimensional Signals and Systems, Guest Editors: S. Basu and M. N. S. Swamy. 49(6), 753–767 (2002)

    Article  Google Scholar 

  22. Plotnik, A.M., Longman, R.W.: Subtleties in the use of zero-phase low-pass filtering and cliff filtering in learning control. Adv. Astronaut. Sci. 103, 673–692 (1999)

    Google Scholar 

  23. Lewis, A.S., Overton, M.L.: Eigenvalue optimization. Acta. Numerica. 149–190 (1996)

  24. Jang, H.S., Longman, R.W.: A new learning control law with monotonic decay of the tracking error norm. In: Proceedings of the Thirty-Second Annual Allerton conference on Communication, Control, and Computing, Monticello, IL, September, 1994, pp. 314–323

  25. Jang, H.S., Longman, R.W.: Design of digital learning controllers using a partial isometry. Adv. Astronaut. Sci. 93, 137–152 (1996)

    Google Scholar 

  26. Owens, D.H., Amann, N.: Norm-optimal iterative learning control. University of Exeter, Internal Report Series of the Centre for Systems and Control Engineering (1994)

    Google Scholar 

  27. Phan, M.Q., Frueh, J.A.: System identification and learning control. Chapter 15. In: Bien, Z., Xu, J. (eds.) Iterative Learning Control: Analysis, Design, Integration, and Applications, pp. 285–306. Kluwer Academic Publishing, Norwell (1998)

    Chapter  Google Scholar 

  28. Bao, J., Longman, R.W.: Unification and robustification of iterative learning control laws. Adv. Astronaut. Sci. 136, 727–745 (2010)

    Google Scholar 

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Acknowledgments

This work was supported by Thailand Research Fund (TRF), Office of the Higher Education Commission (OHEC), and King Mongkut’s University of Technology Thonburi (KMUTT) under grant number RSA6180079.

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Correspondence to Richard W. Longman.

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Panomruttanarug, B., Longman, R.W. & Phan, M.Q. Steady State Frequency Response Design of Finite Time Iterative Learning Control. J Astronaut Sci 67, 571–594 (2020). https://doi.org/10.1007/s40295-019-00198-9

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