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Tracking Control of a Mobile Robot with Kinematic Uncertainty Using Neural Networks

  • An-Min Zou
  • Zeng-Guang Hou
  • Min Tan
  • Xi-Jun Chen
  • Yun-Chu Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4234)

Abstract

In this paper, a kinematic controller based on input-output linearization plus neural network (NN) controller is presented for tracking control of a mobile robot with kinematic uncertainty. The NN controller, whose parameters are tuned on-line, can deal with the uncertainty imposed on the kinematics model of mobile robots. The stability of the proposed approach is guaranteed by the Lyapunov theory. Simulation results show the efficiency of the proposed approach.

Keywords

Mobile Robot Tracking Control Neural Network Control Sigmoid Activation Function Neural Network Controller 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Kanayama, Y., Kimura, Y., Miyazaki, F., Noguchi, T.: A Stable Tracking Control Method for an Autonomous Mobile Robot. In: Proceedings of IEEE International Conference on Robotics and Automation, vol. 1, pp. 384–389 (1990)Google Scholar
  2. 2.
    Yang, J.M., Kim, J.H.: Sliding Mode Control for Trajectory Tracking of Nonholonomic Wheeled Mobile Robots. IEEE Transactions on Robotics and Automation 15(3), 578–587 (1999)CrossRefGoogle Scholar
  3. 3.
    Anfbal, O., Guillermo, H.: Stability Analysis of Mobile Robot Tracking. In: Proceedings of 1995 IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 3, pp. 461–466 (1995)Google Scholar
  4. 4.
    Yuan, G., Yang, S.X., Mittal, G.S.: Tracking Control of a Mobile Robot Using a Neural Dynamics based Approach. In: Proceedings of IEEE International Conference on Robotics and Automation, vol. 1, pp. 163–168 (2001)Google Scholar
  5. 5.
    Fierro, R., Lewis, F.L.: Control of a Nonholonomic Mobile Robot Using Neural Networks. IEEE Transactions on Neural Networks 9(4), 589–600 (1998)CrossRefGoogle Scholar
  6. 6.
    Zou, A., Hou, Z., Tan, M., Zhao, Z.: Tracking Control of a Nonholonomic Mobile Robot Using a Fuzzy-Based Approach. In: ICNC 2006-FSKD 2006 (accepted, 2006)Google Scholar
  7. 7.
    Zou, A., Hou, Z., Wang, H., Tan, M.: Stabilizing Control of a Nonholonomic Mobile Robot: A Fuzzy-Based Approach. In: Proceeding of the International Conference on Sensing, Computing, and Automation, pp. 3219–3224 (2006)Google Scholar
  8. 8.
    Kim, D.H., Oh, J.H.: Tracking Control of a Two-wheeled Mobile Robot Using Input-output Linearization. Control Engineering Practice 7(3), 369–373 (1999)CrossRefMathSciNetGoogle Scholar
  9. 9.
    Lewis, F.L., Yesidirek, A., Liu, K.: Multilayer Neural-Net Robot Controller with Guaranteed Tracking Performance. IEEE Transactions on Neural Networks 7(2), 388–399 (1996)CrossRefGoogle Scholar
  10. 10.
    Homik, K., Stinchcombe, M., White, H.: Multilayer Feedforward Networks are Universal Approximators. Neural Networks 2, 359–366 (1989)CrossRefGoogle Scholar
  11. 11.
    Sastry, S., Bodson, M.: Adaptive Control: Stability, Convergence, and Robustness. Prentice-Hall, Englewood Cliffs (1989)MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • An-Min Zou
    • 1
  • Zeng-Guang Hou
    • 1
  • Min Tan
    • 1
  • Xi-Jun Chen
    • 2
  • Yun-Chu Zhang
    • 1
    • 3
  1. 1.Laboratory of Complex Systems and Intelligence ScienceInstitute of Automation, The Chinese Academy of SciencesBeijingChina
  2. 2.Institute of Automation, The Chinese Academy of SciencesBeijingChina
  3. 3.School of Information and Electric EngineeringShandong University of Architecture and EngineeringJinanChina

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