Skip to main content
SpringerLink
Log in

Experimental implementation of nonlinear TORA system and adaptive backstepping controller design

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

The purpose of this paper is to design an adaptive controller and system experimental implementation for nonlinear translational oscillations with a rotational actuator (TORA) system. A wavelet-based neural network (WNN) is proposed to develop an adaptive backstepping control scheme, called ABCWNN for TORA system. To ensure the stability of the controlled system, a compensated controller is designed to enhance the control performance. Based on its universal approximation ability, we use a WNN to estimate the system uncertainty including frictional forces, external disturbance, and parameter variance. According to the estimations of the WNNs, the ABCWNN control is developed via a backstepping design procedure such that the system outputs follow the desired trajectories. For system development, the effects of frictional forces are discussed and solved using the estimation of the WNN. The effectiveness of the proposed control scheme for TORA system is verified by numerical simulation and experimental results.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

References

  1. Adlgostar R, Azimian H, Taghirad HD (2006) Robust H∞, H2/H∞ controller for rotational/translational actuator (RTAC). In: IEEE Proceedings of the international conference on control applications. pp 4–6

  2. Bupp RT, Bernstein DS (1996) Experimental implementation of integrator backstepping and passive nonlinear controllers on the RTAC tested. In: IEEE International conference on control application. pp 279–284

  3. Bupp RT et al (1998) Special issue: a benchmark problem for nonlinear control design. In: Bupp RT, Bernstein DS (eds) Int. J. of Robust Nonlinear Control, vol 8, no 4. Wiley, pp 305–457

  4. Chang WJ, Wu SM (2003) State variance constrained fuzzy controller design for nonlinear TORA systems with minimizing control input energy. In: IEEE Conference on robotics and automation. pp 2616–2621

  5. Chen YC, Teng CC (1995) A model reference control structure using a fuzzy neural network. Fuzzy Sets Syst 73:291–312

    Article  MathSciNet  MATH  Google Scholar 

  6. Hensen RHA, Santen RAV (2002) Controlled mechanical systems with friction. University Press Facilities, Eindhoven

    Google Scholar 

  7. Ho DC, Zhang PA, Xu J (2001) Fuzzy wavelet networks for function learning. IEEE Trans Fuzzy Syst 9(1):200–211

    Article  Google Scholar 

  8. Hornik K, Stinchombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Netw 2:359–366

    Article  Google Scholar 

  9. Hoyakimyan N, Yang BJ, Calise AJ (2006) Adaptive output feedback control methodology applicable to non-minimum phase nonlinear systems. Automatica 42:513–522

    Article  Google Scholar 

  10. Jankovic M, Fontaine D, Kokotovic PV (1996) TORA example: cascade-and passivity-based control designs. IEEE Trans Control Syst Technol 4:292–297

    Article  Google Scholar 

  11. Juang CF, Cheng CN, Chen TM (2009) Speech detection in noisy environments by wavelet energy-based recurrent neural fuzzy network. Expert Syst Appl 36(1):321–332

    Article  Google Scholar 

  12. Karagiannis D (2005) Nonlinear adaptive control design with applications. Ph. D. Dissertation for London of University

  13. Khalil HK (2000) Nonlinear systems, 3rd edn. Prentice Hall, NJ

    Google Scholar 

  14. Lee CH, Teng CC (2000) Identification and control of dynamic system using recurrent fuzzy neural networks. IEEE Trans Fuzzy Syst 8(4):349–366

    Article  Google Scholar 

  15. Lee CH (2004) Stabilization of nonlinear non-minimum phase systems: adaptive parallel approach using recurrent fuzzy neural network. IEEE Trans Syst Man Cybern B 34(2):1075–1088

    Article  Google Scholar 

  16. Lee CH, Pan HY, Chang HH, Wang BH (2006) Decoupled adaptive type-2 fuzzy controller (DAT2FC) design for nonlinear TORA systems In: IEEE International conference on fuzzy systems, Vancouver, Canada. pp 506–512

  17. Lee CH, Wang BH (2009) Adaptive supervisory WCMAC neural network controller (SWC) for nonlinear systems. Soft Comput 13(1):1–12

    Article  Google Scholar 

  18. Lewis FL, Yesildirek A, Liu K (1996) Multilayer neural-net robot controller with guaranteed tracking performance. IEEE Trans Neural Netw 7(2):388–399

    Article  Google Scholar 

  19. Lin CJ (2009) Nonlinear systems control using self-constructing wavelet networks. J Appl Soft Comput 9:71–79

    Article  Google Scholar 

  20. Lin CJ, Chin CC (2004) Prediction and identification using wavelet-based recurrent fuzzy neural networks. IEEE Trans Syst Man Cybern B 34(5):2144–2154

    Article  Google Scholar 

  21. Olsson H (1996) Control systems with friction. Ph.D. paper, Lund Institute of Techn, Sweden

  22. Pavlov A, Janssen B, van de Wouw N, Nijmeijer H (2007) Experimental output regulation for a nonlinear benchmark system. IEEE Trans Control Syst Technol 15:786–793

    Article  Google Scholar 

  23. Qaiser N, Iqbal N (2005) TORA stabilization via dynamic surface control technique. In: IEEE International conference on emerging technologies. pp 488–493

  24. Rosales A, Scaglia G, Mut V, Sciascio FD (2006) Controller designed by means of numeric methods for a benchmark problem: RTAC (Rotational Translational Actuator). IEEE Conf Robot Automot Mech 1:97–104

    Google Scholar 

  25. Sousa CD, Hemerly JEM, Calvao RH (2002) Adaptive control for mobile robot using wavelet networks. IEEE Trans Syst Man Cybern B Cybern 32(4):493–504

    Article  Google Scholar 

  26. Tavakoli M, Taghirad HD, Abrishamchian M (2005) Identification and robust H∞ control of the rotational/translational actuator system. Int J Control Autom Syst 3(3):387–396

    Google Scholar 

  27. Wai RJ, Duan RY, Lee JD, Chang HH (2003) Wavelet neural network control for induction motor drive using sliding-mode design technique. IEEE Trans Ind Electron 50(4):733–748

    Article  Google Scholar 

  28. Yesildirek A, Lewis FL (1995) Feedback linearization using neural networks. Automatica 31:1659–1664

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgments

The authors would like to thank the Associate Editor and anonymous reviewers for their insightful comments and valuable suggestions. This work was supported in part by the National Science Council, Taiwan, R.O.C., under contracts NSC-95-2221-E-155-068-MY2.

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Yuan Ze University, Chungli, Taoyuan, Taiwan, Republic of China

    Ching-Hung Lee & Sheng-Kai Chang

Authors
  1. Ching-Hung Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sheng-Kai Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ching-Hung Lee.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Lee, CH., Chang, SK. Experimental implementation of nonlinear TORA system and adaptive backstepping controller design. Neural Comput & Applic 21, 785–800 (2012). https://doi.org/10.1007/s00521-010-0515-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-010-0515-0

Keywords

Search

Navigation