Skip to main content

Abstract

In the chapter, a number of intelligent control approaches have been investigated. First, the modeling and control method using the Least Squares Support Vector Machine (LS-SVM) have been utilized to design efficient model free control. Then, we further study the universal functional approximation of fuzzy logic and neural networks. All these intelligent control methods employ a systematic online adaptation mechanism without prepared off line learning.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 159.99
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

References

  1. Anupoju, C.M., Su, C.Y., Oya, M.: Adaptive motion tracking control of uncertain nonholonomic mechanical systems including actuator dynamics. IEE Proc., Control Theory Appl. 152(5), 575–580 (2005)

    Article  Google Scholar 

  2. Bi, D., Li, Y.F., Tso, S.K., Wang, G.L.: Friction modeling and compensation for haptic display based on support vector machine. IEEE Trans. Ind. Electron. 51(2), 491–500 (2004)

    Article  Google Scholar 

  3. Chang, Y.C., Chen, B.S.: Robust tracking designs for both holonomic and nonholonomic constrained mechanical systems: adaptive fuzzy approach. IEEE Trans. Fuzzy Syst. 8, 46–66 (2000)

    Article  Google Scholar 

  4. Dong, W., Xu, Y., Huo, W.: Trajectory tracking control of dynamics nonholonomic systems with unknown dynamics. Int. J. Robust Nonlinear Control 9, 905–922 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  5. Ge, S.S., Wang, C.: Adaptive neural control of uncertain MIMO nonlinear systems. IEEE Trans. Neural Netw. 15(3), 674–692 (2004)

    Article  Google Scholar 

  6. Ge, S.S., Zhang, J.: Neural network control of nonaffine nonlinear system with zero dynamics by state and output feedback. IEEE Trans. Neural Netw. 14(4), 900–918 (2003)

    Article  Google Scholar 

  7. Ge, S.S., Lee, T.H., Harris, C.J.: Adaptive Neural Network Control of Robot Manipulators. World Scientific, London (1998)

    Book  Google Scholar 

  8. Ge, S.S., Hang, C.C., Zhang, T.: Adaptive neural network control of nonlinear systems by state and output feedback. IEEE Trans. Syst. Man Cybern., Part B 29(6), 818–828 (1999)

    Article  Google Scholar 

  9. Ge, S.S., Hang, C.C., Lee, T.H., Zhang, T.: Stable Adaptive Neural Network Control. Kluwer Academic, Norwell (2001)

    Google Scholar 

  10. Ge, S.S., Yang, C., Lee, T.H.: Adaptive predictive control using neural network for a class of pure-feedback systems in discrete-time. IEEE Trans. Neural Netw. 19(9), 1599–1614 (2008)

    Article  Google Scholar 

  11. Hahn, W.: Stability of Motion. Springer, Berlin (1967)

    MATH  Google Scholar 

  12. Han, H., Su, C., Stepanenko, Y.: Adaptive control of a class of nonlinear systems with nonlinearly parameterized fuzzy approximators. IEEE Trans. Fuzzy Syst. 9(2), 315–323 (2001)

    Article  Google Scholar 

  13. Hovakimyan, N., Nardi, F., Calise, A.J.: A novel error observer-based adaptive output feedback approach for control of uncertain systems. IEEE Trans. Autom. Control 47(8), 1310–1314 (2002)

    Article  MathSciNet  Google Scholar 

  14. Karnopp, D.: Computer simulation of stick-slip friction in mechanical dynamic systems. ASME J. Dyn. Syst. Meas. Control 107, 100–103 (1985)

    Article  Google Scholar 

  15. Lewis, F.L., Yesildirek, A., Liu, K.: Multilayer neural network robot controller with guaranteed tracking performance. IEEE Trans. Neural Netw. 7(2), 388–399 (1996)

    Article  Google Scholar 

  16. Li, Z., Gu, J., Ming, A., Xu, C., Shimojo, M.: Intelligent complaint force/motion control of nonholonomic mobile manipulator working on the non-rigid surface. Neural Comput. Appl. 15(3–4), 204–216 (2006)

    Google Scholar 

  17. Li, Z., Yang, C., Gu, J.: Neuro-adaptive compliant force/motion control for uncertain constrained wheeled mobile manipulator. Int. J. Robot. Autom. 22(3), 206–214 (2007)

    Google Scholar 

  18. Li, Z., Chen, W., Luo, J.: Adaptive compliant force–motion control of coordinated nonholonomic mobile manipulators interacting with unknown non-rigid environments. Neurocomputing 71(7–9), 1330–1344 (2008)

    Article  Google Scholar 

  19. Lin, W., Qian, C.: Adding one power integrator: a tool for global stabilization of high-order cascade nonlinear systems. Syst. Control Lett. 39, 339–351 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  20. Loreto, G., Garrido, R.: Stable neurovisual servoing for robot manipulators. IEEE Trans. Neural Netw. 17(4), 953–965 (2006)

    Article  Google Scholar 

  21. Lu, G., Song, J., Hua, L., Sun, C.: Inverse system control of nonlinear systems using LS-SVM. In: Proceedings of the 26th Chinese Control Conference, China, 2007, pp. 233–236 (2007)

    Google Scholar 

  22. Munkres, J.R.: Analysis on Manifolds. Addison-Wesley, Reading (1991)

    MATH  Google Scholar 

  23. Suykens, J.A.K., Vandewalle, J., Moor, B.D.: Optimal control by least squares support vector machines. Neural Netw. 14(1), 23–35 (2001)

    Article  Google Scholar 

  24. Vapnik, V.: An overview of statistical learning theory. IEEE Trans. Neural Netw. 10(5), 955–999 (1999)

    Article  Google Scholar 

  25. Vapnik, V.N.: Statistical Learning Theory. Springer, New York (1998)

    MATH  Google Scholar 

  26. Wang, G.L., Li, Y.F., Bi, D.X.: Support vector machine networks for friction modeling. IEEE/ASME Trans. Mechatron. 9(3), 601–606 (2004)

    Article  Google Scholar 

  27. Wang, J., Chen, Q., Chen, Y.: RBF kernel based support vector machine with universal approximation and its application. In: Support Vector Machines, Part III. Lecture Notes in Computer Science, vol. 3173, pp. 512–517 (2004)

    Google Scholar 

  28. Wang, L.: Adaptive Fuzzy Systems and Control, Design, and Stability Analysis. Prentice Hall, Englewood Cliffs (1994)

    Google Scholar 

  29. Xu, J., Chen, S.: Adaptive control of a class of nonlinear discrete-time systems using support vector machine. In: Proceedings of the 5th World Congress on Intelligent Control and Automation, China, 2004, pp. 440–443 (2004)

    Google Scholar 

  30. Yang, C., Ge, S.S., Xiang, C., Chai, T.Y., Lee, T.H.: Output feedback NN control for two classes of discrete-time systems with unknown control directions in a unified approach. IEEE Trans. Neural Netw. 19(11), 873–1886 (2008)

    Google Scholar 

  31. Zhang, H.R., Wang, X.D., Zhang, C.J., Cai, X.S.: Robust identification of non-linear dynamic systems using support vector machine. IEE Proc. Sci. Meas. Technol. 153(3), 125–129 (2006)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag London

About this chapter

Cite this chapter

Li, Z., Yang, C., Fan, L. (2013). Intelligent Control. In: Advanced Control of Wheeled Inverted Pendulum Systems. Springer, London. https://doi.org/10.1007/978-1-4471-2963-9_7

Download citation

  • DOI: https://doi.org/10.1007/978-1-4471-2963-9_7

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-2962-2

  • Online ISBN: 978-1-4471-2963-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics