Advertisement

ESO Architectures in the Trajectory Tracking ADR Controller for a Mechanical System: A Comparison

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1196)

Abstract

Proper operation of the Active Disturbance Rejection (ADR) controller requires a precise determination of the so-called total disturbance affecting the considered dynamical system, usually estimated by the Extended State Observer (ESO). The observation quality of total disturbance has a significant impact on the control error values, making room for a potential improvement of control system performance using different structures of ESO. In this article, we provide a quantitative comparison between the Luenberger and Astolfi/Marconi (AM) observers designed for three different extended state representations and utilized in the trajectory tracking ADR controller designed for a mechanical system. Included results were obtained in the simple simulation case, followed by the experimental validation on the main axis of a telescope mount.

Keywords

Active Disturbance Rejection Control (ADRC) Extended State Observer (ESO) Trajectory tracking Mechanical system 

References

  1. 1.
    Astolfi, D., Marconi, L.: A high-gain nonlinear observer with limited gain power. IEEE Trans. Autom. Control 60(11), 3059–3064 (2015)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Canudas Wit, C., Olsson, H., Astrom, K.J., Lischinsky, P.: A new model for control of systems with friction. IEEE Trans. Autom. Control 40(3), 419–425 (1995).  https://doi.org/10.1109/9.376053MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Gao, Z.: Active disturbance rejection control: a paradigm shift in feedback control system design. In: 2006 American Control Conference, p. 7 (2006)Google Scholar
  4. 4.
    Han, J.: From pid to active disturbance rejection control. IEEE Trans. Industr. Electron. 56(3), 900–906 (2009)CrossRefGoogle Scholar
  5. 5.
    Huang, Y., Xue, W.: Active disturbance rejection control: methodology and theoretical analysis. ISA Trans. 53(4), 963–976 (2014). Disturbance Estimation and MitigationMathSciNetCrossRefGoogle Scholar
  6. 6.
    Khalil, H.K.: Nonlinear Systems, 3rd edn. Prentice Hall, Upper Saddle River (2002)zbMATHGoogle Scholar
  7. 7.
    Khalil, H.K., Praly, L.: High-gain observers in nonlinear feedback control. Int. J. Robust Nonlinear Control 24(6), 993–1015 (2014)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Kozlowski, K., Pazderski, D., Krysiak, B., Jedwabny, T., Piasek, J., Kozlowski, S., Brock, S., Janiszewski, D., Nowopolski, K.: High precision automated astronomical mount. In: Szewczyk, R., Zieliński, C., Kaliczyńska, M. (eds.) Automation 2019, pp. 299–315. Springer, Cham (2020)CrossRefGoogle Scholar
  9. 9.
    Łakomy, K., Michałek, M.M.: Robust output-feedback vfo-adr control of underactuated spatial vehicles a task of following the non-parametrized path (2020). arXiv:2001.01963 [eess.SY]Google Scholar
  10. 10.
    Madonski, R., Ramirez-Neria, M., Stanković, M., Shao, S., Gao, Z., Yang, J., Li, S.: On vibration suppression and trajectory tracking in largely uncertain torsional system: an error-based ADRC approach. Mech. Syst. Signal Process. 134, 106300 (2019)CrossRefGoogle Scholar
  11. 11.
    Martinez-Vazquez, D.L., Rodriguez-Angeles, A., Sira-Ramirez, H.: Robust GPI observer under noisy measurements. In: 2009 6th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE), pp. 1–5 (2009)Google Scholar
  12. 12.
    Michałek, M.M.: Robust trajectory following without availability of the reference time-derivatives in the control scheme with active disturbance rejection. In: 2016 American Control Conference (ACC), pp. 1536–1541 (2016)Google Scholar
  13. 13.
    Michałek, M.M., Łakomy, K., Adamski, W.: Robust output-feedback cascaded tracking controller for spatial motion of anisotropically-actuated vehicles. Aerosp. Sci. Technol. 92, 915–929 (2019)CrossRefGoogle Scholar
  14. 14.
    Patelski, R., Pazderski, D.: Tracking control for a cascade perturbed control system using the active disturbance rejection paradigm. Arch. Control Sci. 29(2), 387–408 (2019)MathSciNetzbMATHGoogle Scholar
  15. 15.
    Peng, Z., Wang, J.: Output-feedback path-following control of autonomous underwater vehicles based on an extended state observer and projection neural networks. IEEE Trans. Syst. Man Cybern. B Cybern. Syst. 48(4), 535–544 (2018)CrossRefGoogle Scholar
  16. 16.
    Piasek, J., Patelski, R., Pazderski, D., Kozłowski, K.: Identification of a dynamic friction model and its application in a precise tracking control. Acta Polytechnica Hungarica 16(10), 83–99 (2019)CrossRefGoogle Scholar
  17. 17.
    Wang, L., Astolfi, D., Marconi, L., Su, H.: High-gain observers with limited gain power for systems with observability canonical form. Automatica 75, 16–23 (2017)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Xue, W., Madonski, R., Lakomy, K., Gao, Z., Huang, Y.: Add-on module of active disturbance rejection for set-point tracking of motion control systems. IEEE Trans. Ind. Appl. 53(4), 4028–4040 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Poznań University of TechnologyPoznańPoland

Personalised recommendations