Skip to main content
SpringerLink
Log in

Adaptive robust control for servo manipulators

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

Abstract

In this paper, an adaptive robust control scheme is developed which is suitable for the control of a class of uncertain nonlinear systems, typical of many servo manipulators. The control scheme is comprised of a model reference adaptive controller (MRAC) augmented with a nonlinear compensator based on an adaptive radial basis function (RBF). The RBF compensator is used to neutralise the effects of uncertain and possibly nonlinear dynamics, so that the equivalent system as seen by the MRAC is reduced to one without significant unstructured modelling errors. A stability analysis is provided to show the uniform stability and the asymptotic tracking capabilities of the proposed control system. Real-time experiment results verify the effectiveness of the control scheme.

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.

Similar content being viewed by others

References

  1. Zalzala AMS (1996) Model-based adaptive neural structures for robotic control. In: Zalzala AMS (ed) Neural Networks for Robotic Control, Ellis Horwood, London

  2. Pagilla P, Tomizuka M (1995) Adaptive control of two robot arms carrying an unknown object. In: Proceedings of the 1995 IEEE International Conference on Robotics and Automation, Washington, DC, 11-14 May 1995

  3. Lee TH, Tan KK and Lee MW (1998) A variable structured-augmented adaptive controller for a gyro-mirror LOS stabilization platform. Mechatronics, 8(1):47–64

  4. Tan KK, Khoh CJ (1999) Variable structure augmented adaptive control of a LOS system. Technical Report 9904, National University of Singapore

  5. Callan R (1999) The essence of neural networks. Prentice Hall, London

  6. Warwick K (1996) An overview of neural networks in control applications. In: Zalzala AMS (ed) Neural networks for robotic control, Ellis Horwood, London

  7. Morris AS, Khemaissia S (1996) Artificial neural network based intelligent robot dynamic control. In: Zalzala AMS (ed) Neural networks for robotic control, Ellis Horwood, London

  8. Astrom KJ, Wittermark B (1989) Adaptive control. Addison-Wesley, Reading, MA

  9. Lee TH, Tan KK and Lee MW (2001) Composite control of a gyro mirror line-of-sight stabilization platform design and auto-tuning. ISA Trans 40(2):155–171

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, 117576, Singapore, Singapore

    C. J. Khoh & K. K. Tan

Authors
  1. C. J. Khoh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. K. K. Tan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to K. K. Tan.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Khoh, C.J., Tan, K.K. Adaptive robust control for servo manipulators. Neural Comput & Applic 12, 178–184 (2003). https://doi.org/10.1007/s00521-003-0380-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-003-0380-1

Keywords

Search

Navigation