Skip to main content
SpringerLink
Log in

Intelligent Control of Mechatronic Systems

  • Chapter
  • First Online:
Intelligent Optimal Adaptive Control for Mechatronic Systems

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 120))

Abstract

Among the variety of methods for mechatronic systems control, the intelligent control uses modern algorithms that compensate for the nonlinearity of controlled systems. These algorithms can adapt their parameters to variable operating conditions and comprise artificial intelligence methods such as artificial neural networks, and fuzzy logic algorithms. A distinction can be made between fuzzy control and neural control based on the type of artificial intelligence algorithms.

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

Access this chapter

Log in via an institution

Institutional subscriptions

References

  1. Astrom, K.J., Wittenmark, B.: Adaptive Control. Addison-Wesley, New York (1979)

    Google Scholar 

  2. Barto, A., Sutton, R., Anderson, C.: Neuronlike adaptive elements that can solve difficult learning problems. IEEE Trans. Syst. Man Cybern. Syst. 13, 834–846 (1983)

    Google Scholar 

  3. Burns, R.S.: Advanced Control Engineering. Butterworth-Heinemann, Oxford (2001)

    Google Scholar 

  4. Fabri, S., Kadirkamanathan, V.: Dynamic structure neural networks for stable adaptive control of nonlinear systems. IEEE Trans. Neural. Netw. 12, 1151–1167 (1996)

    Article  Google Scholar 

  5. Ferrari, S., Stengel, R.F.: An adaptive critic global controller. In: Proceedings of American Control Conference, vol. 4, pp. 2665–2670. Anchorage, Alaska (2002)

    Google Scholar 

  6. Giergiel, J., Hendzel, Z., ylski, W.: Kinematics, Dynamics and Control of Wheeled Mobile Robots in Mechatronic Aspect. (in Polish) Faculty IMiR AGH, Krakow (2000)

    Google Scholar 

  7. Giergiel, J., Hendzel, Z., ylski, W.: Modeling and Control of Wheeled Mobile Robots. (in Polish) Scientific Publishing PWN, Warsaw (2002)

    Google Scholar 

  8. Gierlak, P., Szuster, M., ylski, W.: Discrete Dual-heuristic Programming in 3DOF Manipulator Control. Lecture Notes in Artificial Intelligence. vol. 6114, 256–263 (2010)

    Google Scholar 

  9. Hendzel, Z.: An adaptive critic neural network for motion control of a wheeled mobile robot. Nonlinear Dyn. 50, 849–855 (2007)

    Article  MATH  Google Scholar 

  10. Hendzel, Z., Burghardt, A.: Behavioural Control of Wheeled Mobile Robots. (in Polish) Rzeszow University of Technology Publishing House, Rzeszow (2007)

    Google Scholar 

  11. Hendzel, Z., Szuster, M.: A dynamic structure neural network for motion control of a wheeled mobile robot. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J. (eds.) Computational Inteligence: Methods ans Applications, pp. 365–376. EXIT, Warsaw (2008)

    Google Scholar 

  12. Hendzel, Z., Szuster, M.: Discrete neural dynamic programming in wheeled mobile robot control. Commun. Nonlinear. Sci. Numer. Simul. 16, 2355–2362 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  13. Hendzel, Z., Trojnacki, M.: Neural Network Control of Mobile Wheeled Robots. (in Polish) Rzeszow University of Technology Publishing House, Rzeszow (2008)

    Google Scholar 

  14. Igelnik, B., Pao, Y.-H.: Stochastic choice of basis functions in adaptive function approximation and the functional-link net. IEEE Trans. Neural. Netw. 6, 1320–1329 (1995)

    Article  Google Scholar 

  15. Jamshidi, M., Zilouchian, A.: Intelligent Control Systems Using Soft Computing Methodologies. CRC Press, London (2001)

    Google Scholar 

  16. Janecki, D.: The Role of Uniformly Excitation Signals in Adaptive Control Systems. (in Polish) IPPT PAS, Warsaw (1995)

    Google Scholar 

  17. Jankowski, N.: Ontogenic Neural Networks. (in Polish) Exit, Warsaw (2003)

    Google Scholar 

  18. Kecman, V.: Learning and Soft Computing. MIT Press, Cambridge (2001)

    MATH  Google Scholar 

  19. Kim, Y.H., Lewis, F.L.: A dynamical recurrent neural-network-based adaptive observer for a class of nonlinear systems. Automatica 33, 1539–1543 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  20. Kozowski, K., Dutkiewicz, P., Wrblewski, W.: Modeling and Control of Robots. (in Polish) Scientific Publishing PWN, Warsaw (2003)

    Google Scholar 

  21. Levis, F.L., Liu, K., Yesildirek, A.: Neural net robot controller with guaranted tracking performance. IEEE Trans. Neural. Netw. 6, 703–715 (1995)

    Article  Google Scholar 

  22. Liu, G.P.: Nonlinear Identification and Control. Springer, London (2001)

    Book  Google Scholar 

  23. Niderliski, A., Mociski, J., Ogonowski, Z.: Adaptive Regulation. (in Polish) PWN, Warsaw (1995)

    Google Scholar 

  24. Ortega, R., Spong, M.W.: Adaptive motion control of rigid robots: a tutorial. Automatica 25, 877–888 (1989)

    Article  MathSciNet  MATH  Google Scholar 

  25. Osowski, S.: Neural Networks - An Algorithmic Approach. (in Polish) WNT, Warsaw (1996)

    Google Scholar 

  26. Powell, W.B.: Approximate Dynamic Programming: Solving the Curses of Dimensionality. Princeton, Wiley-Interscience (2007)

    Book  MATH  Google Scholar 

  27. Prokhorov, D., Wunch, D.: Adaptive critic designs. IEEE Trans. Neural. Netw. 8, 997–1007 (1997)

    Article  Google Scholar 

  28. Si, J., Barto, A.G., Powell, W.B., Wunsch, D.: Handbook of Learning and Approximate Dynamic Programming. IEEE Press, Wiley-Interscience (2004)

    Book  Google Scholar 

  29. Slotine, J.J., Li, W.: Applied Nonlinear Control. Prentice Hall, New Jersey (1991)

    MATH  Google Scholar 

  30. Spong, M.W., Vidyasagar, M.: Robot Dynamics and Control. (in Polish) WNT, Warsaw (1997)

    Google Scholar 

  31. Spooner, J.T., Passio, K.M.: Stable adaptive control using fuzzy systems and neural networks. IEEE Trans. Fuzzy. Syst. 4, 339–359 (1996)

    Article  Google Scholar 

  32. Syam, R., Watanabe, K., Izumi, K.: Adaptive actor-critic learning for the control of mobile robots by applying predictive models. Soft. Comput. 9, 835–845 (2005)

    Article  MATH  Google Scholar 

  33. Visnevski, N., Prokhorov, D.: Control of a nonlinear multivariable system with adaptive critic designs. In: Proceedings of Artificial Neural Networks in Engineering. vol. 6, pp. 559–565 (1996)

    Google Scholar 

  34. Zylski, W., Gierlak, P.: Tracking Control of Robotic Manipulator. (in Polish) Rzeszow University of Technology Publishing House, Rzeszow (2014)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Applied Mechanics and Robotics, Faculty of Mechanical Engineering and Aeronautics, Rzeszow University of Technology, Rzeszow, Poland

    Marcin Szuster & Zenon Hendzel

Authors
  1. Marcin Szuster
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zenon Hendzel
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Marcin Szuster .

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Szuster, M., Hendzel, Z. (2018). Intelligent Control of Mechatronic Systems. In: Intelligent Optimal Adaptive Control for Mechatronic Systems. Studies in Systems, Decision and Control, vol 120. Springer, Cham. https://doi.org/10.1007/978-3-319-68826-8_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-68826-8_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68824-4

  • Online ISBN: 978-3-319-68826-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation