Tracking Control Based on Neural Network for Robot Manipulator

  • Murat Sonmez
  • Ismet Kandilli
  • Mehmet Yakut
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3949)


In this paper, a control algorithm based on neural networks is presented. This control algorithm has been applied to a robot arm which has a highly nonlinear structure. The model based approaches for robot control require high computational time and can result in a poor control performance, if the specific model structure selected does not properly reflect all the dynamics. The control technique proposed here has provided satisfactory results. A decentralized model has been assumed here where a controller is associated with each joint and a separate neural network is used to adjust the parameters of each controller. Neural networks have been used to adjust the parameters of the controllers, being the outputs of the neural networks, the control parameters.


Neural Network Tracking Error Robot Manipulator Neural Network Control Neural Network Weight 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Astrom, K.J., Hagglund, T.: Automatic tuning of simple regulators with specification on phase and amplitude margins. Automatica 20, 645 (1984)MathSciNetCrossRefMATHGoogle Scholar
  2. 2.
    Astrom, K.J., Hagglund, T.: PID Controller: Theory, Design, and Tuning, Research Triangle Park, NC, USA (1995)Google Scholar
  3. 3.
    Astrom, K.J., Wittenmark, B.: Adaptive Control. Addition-Wesley, New York (1995)Google Scholar
  4. 4.
    Cha, I., Han, C.: The auto-tuning PID controller using the parameter estimation. In: IEEE=RSJ International Conference on Intelligent Robots and System, p. 46 (1999)Google Scholar
  5. 5.
    Chen, P.C.Y., Mills, J.K., Vukovich, G.: Neural network learning and generalization for performance improvement of industrial robots. In: Canadian Conference on Electrical and Computer Engineering, p. 566 (1996)Google Scholar
  6. 6.
    Clifton, C., Homaifar, A., Bikdash, M.: Design of generalized Sugeno controllers by approximating hybrid fuzzy PID controllers. In: IEEE International Conference on Fuzzy Systems, p. 1906 (1996)Google Scholar
  7. 7.
    Gutierrez, L.B., Lewis, F.L., Lowe, J.A.: Implementation of a neural network tracking controller for a single flexible link: comparison with PD and PID controller. IEEE Trans. Ind. Electron 45, 307 (1998)CrossRefGoogle Scholar
  8. 8.
    Hang, C.C., Astrom, K.J., Ho, W.K.: Refinements of the Ziegler-Nichols tuning formula. IEE Proc. Control Theory Appl. 138, 111 (1991)CrossRefGoogle Scholar
  9. 9.
    Huang, S.J., Lee, J.S.: A stable self-organizing fuzzy controller for robotic motion control. IEEE Trans. Ind. Electron 47, 421 (2000)CrossRefGoogle Scholar
  10. 10.
    Kim, Y.H., Lewis, F.L.: Optimal design of CMAC neural-network controller for robot manipulators. IEEE Trans. Systems Man Cybernet 30, 22 (2000)CrossRefGoogle Scholar
  11. 11.
    Koivo, A.J.: Fundamentals for Control of Robotic Manipulators. Wiley, Chichester (1989)Google Scholar
  12. 12.
    Kuc, T.Y., Han, W.G.: Adaptive PID learning of periodic robot motion. In: IEEE Conference on Decision and Control, p. 186 (1998)Google Scholar
  13. 13.
    Lewis, F.L., Abdallah, C.T., Dawson, D.M.: Control of Robot Manipulators. Macmillan, New york (1993)Google Scholar
  14. 14.
    Lewis, F.L., Yesildirek, A., Liu, K.: Multilayer neural-net robot controller with guaranteed tracking performance. IEEE Trans. Neural Networks 7, 388 (1996)CrossRefGoogle Scholar
  15. 15.
    Li, Y., Ho, Y.K., Chua, C.S.: Model-based PID control of constrained robot in a dynamic environment with uncertainty. In: IEEE International Conference on Control Applications, p. 74 (2000)Google Scholar
  16. 16.
    Lin, F.J., Hwang, W.J., Wai, R.J.: Ultrasonic motor servo drive with on-line trained neural network model-following controller. IEE Proc. Electric Power Appl. 145, 105 (1998)CrossRefGoogle Scholar
  17. 17.
    Misir, D., Malki, H.A., Chen, G.: Graphical stability analysis for a fuzzy PID controlled robot arm model. In: IEEE International Conference on Fuzzy Systems, p. 451 (1998)Google Scholar
  18. 18.
    Schilling, R.J.: Fundamentals of Robotic: Analysis and Control. Prentice-Hall, N.J (1998)Google Scholar
  19. 19.
    Slotine, J.J.E., Li, W.: Applied Nonlinear Control. Prentice-Hall, Englewood Cliffs (1991)MATHGoogle Scholar
  20. 20.
    Sun, D., Mills, J.K.: High-accuracy trajectory tracking of industrial robot manipulator using adaptive-learning schemes. In: American Control Conference, p. 1935 (1999)Google Scholar
  21. 21.
    Taylor, D.: Composite control of direct-drive robots. In: Proceedings of the IEEE Conference on Decision and Control, p. 1670 (1989)Google Scholar
  22. 22.
    Vemuri, A.T., Polycarpou, M.M.: Neural-network-based robust fault diagnosis in robotic systems. IEEE Trans. Neural Networks 8, 1410 (1997)CrossRefGoogle Scholar
  23. 23.
    Yoo, B.K., Ham, W.C.: Adaptive control of robot manipulator using fuzzy compensator. IEEE Trans. Fuzzy Systems 8, 186 (2000)CrossRefGoogle Scholar
  24. 24.
    Nil, M., Sönmez, M., Yüzgeç, U., Çakır, B.: Artificial Neural Network Based Control of 3 DOF Robot Arm. In: Int. Symposium on Intelligent Manufacturing System IMS, pp. 468–474 (2004)Google Scholar
  25. 25.
    Sönmez, M., Yakut, M.: A Robotic System based on Artificial Neural Network. Mechatronics and Robotic, 293–296 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Murat Sonmez
    • 1
  • Ismet Kandilli
    • 2
  • Mehmet Yakut
    • 1
  1. 1.Electronics and Telecommunication DeptKocaeli UniversityKocaeliTurkey
  2. 2.Industrial Electronics Dept.Kocaeli UniversityKocaeliTurkey

Personalised recommendations