Skip to main content
SpringerLink
Log in

Automatic Parameter Identification for Mechatronic Systems

  • Conference paper
  • First Online:
Multibody System Dynamics, Robotics and Control

Abstract

Simulations of the behaviour of complex mechatronic systems require optimal simulation parameters for obtaining realistic results. For highly accurate mechatronic simulations, an algorithm for searching the optimal parameters is required. In the field of robotics the identification based on minimization of the residuum with least square methods is state of the art. This chapter describes a special algorithm for automatic parameter identification for mechatronic systems, based on the theory of genetic optimization, which works also in case of multiple local minima of the simulation error distribution. Nominal parameters of a simulated belt drive are identified in time and frequency domain highly accurate. Special treatment of the simulation error in frequency domain leads to reduced identification effort. Finally, the algorithm for automatic parameter identification searches real robot parameters up to high accuracy. The automatic parameter identification algorithm leads to accurate simulation results, even though the measurement contains noise and also time delays.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Note: Noise is contained in measurement data of mechatronic systems.

  2. 2.

    If the DFT is applied to signals with real values, the result constists of conjugate complex values.

  3. 3.

    Inital population size = 100, \( {{N}_s} \) = 10, \( {{N}_c} \) = 10, \( \xi \) = 0.5, \( {{d}_{{min,0}}} \) = 0.1

References

  1. Arakawa A, Miyata KA (1996) Simultaneous optimization algorithm for determining both mechanical-system and controller parameters for positioning control mechanisms. In: Proceedings of the 4th international workshop on advanced motion control, Tsu, Mie, 1996

    Google Scholar 

  2. Bremer H, Pfeiffer F (1992) Elastische Mehrkörpersysteme. B G Teubner, Stuttgart

    MATH  Google Scholar 

  3. Cepon G, Manin L (2009) Introduction of damping into the flexible multibody belt-drive model: a numerical and experimental investigation. J Sound Vib 324:283–296

    Article  Google Scholar 

  4. Dozier G, Homaifar A, Bikdash S (1998) Artificial potential field-based motion planning/navigation. Dynamic constrained optimization and simple genetic hill climbing. Simul 71:168–181

    Article  MATH  Google Scholar 

  5. Fairweather AJ, Foster MP, Stone DA (2010) VRLA battery parameter, identification using Pseudo Random Binary Sequences (PBRS). In: Proceedings of the 5th IET international conference on power electronics, machines and drives, Brighton, 2010

    Google Scholar 

  6. Fitzpatrick JM, Grefenstette JJ (1988) Genetic algorithms in noisy environments. Mach Learn 3:101–120

    Google Scholar 

  7. Fliess M, Sira-Ramírez H (2003) An algebraic framework for linear identification. ESAIM: Control, Optim Calc Var 9:151–168

    Article  MathSciNet  MATH  Google Scholar 

  8. Fliess M, Join C, Sira-Ramírez H (2008) Non-linear estimation is easy. Int J Model Identif Control 4. doi:10.1504/IJMIC.2008.020996

  9. Gerstmayr J (2009) HOTINT - A C++ environment for the simulation of multibody dynamics systems and finite elements. In: Proceedings of the eccomas thematic conference on multibody dynamics, Warsaw, pp 1–20

    Google Scholar 

  10. Hashim SZM, Tokhi MO, Darus IZM (2006) Active vibration control of flexible structures using genetic optimization. J Low Freq Noise Vib Act Control 25:195–207

    Article  Google Scholar 

  11. Ljung L (1999) System identification theory for the user, 2nd edn. Prentice-Hall, Upper Saddle River

    Google Scholar 

  12. Ludwig R, Gerstmayr J, Augdopler C, Mittermayer C (2008) Realistic robot simulation with flexible components. In: Proceedings of the 5th international conference on computational intelligence, robotics and autonomous systems (CIRAS), (2008), Linz, pp 187–192

    Google Scholar 

  13. Ludwig R, Gerstmayr J, Augdopler C, Mittermayer C (2008) Flexible robot with clearance. In: Proceedings of the 4th European conference on structural control, vol 2, Saint-Petersburg, pp 511–518

    Google Scholar 

  14. Ludwig R, Gerstmayr J (2011) Automatic parameter identification for generic robot models. In: Proceedings of the multibody dynamics eccomas thematics, Brussels, pp 1–16

    Google Scholar 

  15. Martínez JL, Mandow A, Morales J, Pedraza S, García-Cerezo A (2005) Approximating kinematics for tracked mobile robots. Int J Robotics Res 24:867–878

    Article  Google Scholar 

  16. Pfeiffer F, Hölzl J (1995) Parameter identification for industrial robots. In: Proceedings of the IEEE international conference in robotics and automation, Nagoya, Japan, pp 1468–1476

    Google Scholar 

  17. De Queiroz MS (2009) An active identification method of rotor unbalance parameters. J Vib Control 15:1365–1374

    Article  MathSciNet  Google Scholar 

  18. Ramos F, Feliu V, Payo I (2008) Design of trajectories with physical constraints for very lightweight single link flexible Arms. J Vib Control 14:1091–1110

    Article  MathSciNet  MATH  Google Scholar 

  19. Shabana AA (1998) Dynamics of multibody systems, 2nd edn. Cambridge University Press, Cambridge, USA

    MATH  Google Scholar 

  20. Teng TK, Shieh JS, Chen CS (2003) Genetic algorithms applied in online autotuning PID parameters of a liquid – level control system. Trans Inst Meas Control 5:433–450

    Article  Google Scholar 

  21. Xiaomin X, Qing S, Ling Z, Bin Z (2009) Parameter estimation and its sensitivity analysis of the MR damper hysteresis model using a modified genetic algorithm. J Intell Material Syst Struct 20:2089–2100

    Article  Google Scholar 

  22. Zakharov A, Halasz S (2001) Parameter identification of a robot arm using genetic algorithms. Period Polytechn Ser El Eng 45:195–209

    Google Scholar 

  23. Ziegler F (1998) Technische Mechanik der festen und flüssigen Körper. Springer, Wien

    Book  MATH  Google Scholar 

Download references

Acknowledgement

The authors appreciate the support of their work in the framework of the K2-Austrian Center of Competence in Mechatronics, ACCM. The authors like to acknowledge the cooperation on the present subject with the company KEBA AG.

Author information

Authors and Affiliations

  1. Austrian Center of Competence in Mechatronics GmbH, Altenberger Street 69, Linz, 4040, Austria

    Rafael Ludwig & Johannes Gerstmayr

Authors
  1. Rafael Ludwig
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Johannes Gerstmayr
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rafael Ludwig .

Editor information

Editors and Affiliations

  1. Institute for Robotics, Johannes Kepler University Linz, Altenberger Straße 69, Linz, 4040, Austria

    Hubert Gattringer

  2. Linz Center of Mechatronics GmbH, Altenbergerstrasse 69, Linz, 4040, Austria

    Johannes Gerstmayr

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Wien

About this paper

Cite this paper

Ludwig, R., Gerstmayr, J. (2013). Automatic Parameter Identification for Mechatronic Systems. In: Gattringer, H., Gerstmayr, J. (eds) Multibody System Dynamics, Robotics and Control. Springer, Vienna. https://doi.org/10.1007/978-3-7091-1289-2_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-7091-1289-2_12

  • Published:

  • Publisher Name: Springer, Vienna

  • Print ISBN: 978-3-7091-1288-5

  • Online ISBN: 978-3-7091-1289-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation