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Fixed Point Iteration-Based Adaptive Control Improved with Parameter Identification

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Advances in Service and Industrial Robotics (RAAD 2023)

Part of the book series: Mechanisms and Machine Science ((Mechan. Machine Science,volume 135))

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Abstract

This paper reports the freshest element of the chain of investigations tackling the combination of the Fixed Point Iteration-based adaptive controller with parameter identification. Though this controllers does not necessarily use the identified model, it is expected that the use of the more precise model improves its various properties. Simulation investigations are made for a cylindrical robot for replacing the Particle Swarm Optimization with simple regression-based approach. It is concluded that the use of the best identified model still makes it expedient the application the FPI-based adaptivity. The remaining imprecisions seem to be related to the not well balanced structure of the experimentally collected and analyzed data. It can be expected that this approach can be realized real-time with appropriate hardware.

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References

  1. Armstrong, B., Khatib, O., Burdick, J.: The explicit dynamic model and internal parameters of the PUMA 560 arm. In: Proceedings of the IEEE Conference on Robotics and Automation, pp. 510–518 (1986)

    Google Scholar 

  2. Bécsi, T., Szabó, A., Kővári, B., Aradi, S., Gáspár, P.: Reinforcement learning based control design for a floating piston pneumatic gearbox actuator. IEEE Access 8, 147295–147312 (2020). https://doi.org/10.1109/ACCESS.2020.3015576

    Article  Google Scholar 

  3. Corke, P., Armstrong-Helouvry, B.: A search for consensus among model parameters reported for the PUMA 560 robot. In: Proceedings of the IEEE Conference on Robotics and Automation, pp. 1608–1613 (1994)

    Google Scholar 

  4. Csanádi, B., Galambos, P., Tar, J., Györök, G., Serester, A.: A novel, abstract rotation-based fixed point transformation in adaptive control. In: Proceedings of the 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC2018), Miyazaki, Japan, 7–10 October 2018, pp. 2577–2582 (2018)

    Google Scholar 

  5. Deniša, M., Ude, A., Gams, A.: Adaptation of motor primitives to the environment through learning and statistical generalization. In: Borangiu, T. (ed.) Advances in Robot Design and Intelligent Control. AISC, vol. 371, pp. 449–457. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-21290-6_45

    Chapter  Google Scholar 

  6. Dineva, A., Várkonyi-Kóczy, A., Tar, J.: Combination of RFPT-based adaptive control and classical model identification. In: Proceedings of the IEEE 12th International Symposium on Applied Machine Intelligence and Informatics (SAMI 2014), Herl’any, Slovakia, pp. 35–40 (2014)

    Google Scholar 

  7. Hamandi, M., Tognon, M., Franchi, A.: Direct acceleration feedback control of quadrotor aerial vehicles. In: 2020 IEEE International Conference on Robotics and Automation (ICRA), pp. 5335–5341. IEEE (2020)

    Google Scholar 

  8. Issa, H., Tar, J.K.: Improvement of an adaptive robot control by particle swarm optimization-based model identification. Mathematics 10(19) (2022). https://doi.org/10.3390/math10193609

  9. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the 1995 IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)

    Google Scholar 

  10. Kővári, B., Hegedüs, F., Bécsi, T.: Design of a reinforcement learning-based lane keeping planning agent for automated vehicles. Appl. Sci. 10(20) (2020). https://doi.org/10.3390/app10207171

  11. Levenberg, K.: A method for the solution of certain non-linear problems in least squares. Q. Appl. Math. 2, 164–168 (1944)

    Article  MathSciNet  MATH  Google Scholar 

  12. Lyapunov, A.: Stability of Motion. Academic Press, New York (1966)

    MATH  Google Scholar 

  13. Marquardt, D.: An algorithm for least-squares estimation of nonlinear parameters. J. Soc. Ind. Appl. Math. 11(2), 431–441 (1963)

    Article  MathSciNet  MATH  Google Scholar 

  14. Nguyen, C., Antrazi, S., Zhou, Z.L., Campbell, C., Jr.: Adaptive control of a Stewart platform-based manipulator. J. Robot. Syst. 10(5), 657–687 (1993)

    Article  Google Scholar 

  15. Radac, M.B., Precup, R.E., Petriu, E.: Constrained data-driven model-free ILC-based reference input tuning algorithm. Acta Polytechnica Hungarica 12(1), 137–160 (2015)

    Google Scholar 

  16. Slotine, J.J.E., Li, W.: Applied Nonlinear Control. Prentice Hall International Inc., Englewood Cliffs (1991)

    MATH  Google Scholar 

  17. Somló, J., Lantos, B., Pham, T.C.: Advanced robot control (2002)

    Google Scholar 

  18. Spong, M., Ortega, R.: On adaptive inverse dynamics control of rigid robots. IEEE Trans. Autom. Control 35(1), 92–95 (1990). https://doi.org/10.1109/9.45152

    Article  MathSciNet  MATH  Google Scholar 

  19. Tar, J., Bitó, J., Nádai, L., Tenreiro Machado, J.: Robust Fixed Point Transformations in adaptive control using local basin of attraction. Acta Polytechnica Hungarica 6(1), 21–37 (2009)

    Google Scholar 

  20. Tar, J., Rudas, I., Dineva, A., Várkonyi-Kóczy, A.: Stabilization of a modified Slotine-Li adaptive robot controller by robust fixed point transformations. In: Proceedings of Recent Advances in Intelligent Control, Modelling and Simulation, Cambridge, MA, USA, pp. 35–40 (2014)

    Google Scholar 

  21. Varga, B., Tar, J., Horváth, R.: Tuning of dynamic model parameters for adaptive control using particle swarm optimization. In: Szakál, A. (ed.) Proceedings of the IEEE 10th Jubilee International Conference on Computational Cybernetics and Cyber-Medical Systems, ICCC 2022, Reykjavík, Iceland, 6–9 July 2022, pp. 197–202. IEEE Hungary Section, Budapest, Hungary (2022)

    Google Scholar 

  22. Varga, B., Issa, H., Horváth, R., Tar, J.: Sub-optimal solution of the inverse kinematic task of redundant robots without using Lagrange multipliers. Syst. Theory Control Comput. J. 1(2), 40–48 (2021). https://doi.org/10.52846/stccj.2021.1.2.25

    Article  Google Scholar 

  23. Wang, H., Xie, Y.: Adaptive inverse dynamics control of robots with uncertain kinematics and dynamics. Automatica 45(9), 2114–2119 (2009). https://doi.org/10.1016/j.automatica.2009.05.011

    Article  MathSciNet  MATH  Google Scholar 

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Authors and Affiliations

  1. Doctoral School of Applied Informatics and Applied Mathematics, Óbuda University, Bécsi út 96/B, Budapest, 1034, Hungary

    Bence Varga & József K. Tar

  2. Bánki Donát Faculty of Mechanical and Safety Engineering, Óbuda University, Budapest, Hungary

    Bence Varga & Richárd Horváth

  3. John von Neumann Faculty of Informatics, Óbuda University, Budapest, Hungary

    József K. Tar

  4. Antal Bejczy Center of Intelligent Robotics, Óbuda University, Budapest, Hungary

    József K. Tar

Authors
  1. Bence Varga
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  2. József K. Tar
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  3. Richárd Horváth
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Corresponding author

Correspondence to Bence Varga .

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Editors and Affiliations

  1. Department of Automatics, Biocybernetics, and Robotics, Jožef Stefan Institute, Ljubljana, Slovenia

    Tadej Petrič

  2. Department of Automatics, Biocybernetics, and Robotics, Jožef Stefan Institute, Ljubljana, Slovenia

    Aleš Ude

  3. Department of Automatics, Biocybernetics, and Robotics, Jožef Stefan Institute, Ljubljana, Slovenia

    Leon Žlajpah

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Varga, B., Tar, J.K., Horváth, R. (2023). Fixed Point Iteration-Based Adaptive Control Improved with Parameter Identification. In: Petrič, T., Ude, A., Žlajpah, L. (eds) Advances in Service and Industrial Robotics. RAAD 2023. Mechanisms and Machine Science, vol 135. Springer, Cham. https://doi.org/10.1007/978-3-031-32606-6_45

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