Constrained average path tracking for industrial robots

  • J. Y. S. Luh


To eliminate the burden of computing the dynamics while controlling the mechanical manipulator, its dynamic behavior is modeled by a difference equation, which is a natural formulation for computations performed on a digital computer. Quadratic terms representing centrifugal forces are included in the equation to reduce modeling errors. The parameters of the model are estimated based on the minimum variance criterion. The control torque for each joint is computed during each sampling period to minimize the deviation from points on the desired joint path. Desired joint path could be obtained from the Cartesian path using spline function approximations.


Kalman Filter Joint Torque Stochastic Approximation Industrial Robot Joint Path 
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  1. 1.
    D. E. Whitney: Resolved motion rate control of manipulators and human prostheses, IEEE Trans. on Man-Machine Systems, Vol. MMS-10, No. 2, pp. 47–53, June 1969.CrossRefGoogle Scholar
  2. 2.
    D. E. Whitney: The mathematics of coordinated control of prosthetic arms and manipulators, Journal of Dynamic Systems, Measurement, and Control, Trans. of ASME, pp. 303–309, December 1972.Google Scholar
  3. 3.
    R. C. Paul: Modeling, Trajectory Calculation and Servoing of a Computer Controlled Arm,. A.I. Memo 177, Stanford Artificial Intelligence Laboratory, Stanford University, September 1972.Google Scholar
  4. 4.
    B. R. Markiewicz: Analysis of the Computed Torque Drive Method and Comparison with Conventional Position Servo for a Computer-Controlled Manipulator, Technical Memorandum 33–601, Jet Propulsion Laboratory, March 1973.Google Scholar
  5. 5.
    A. K. Bejczy: Robot Arm Dynamics and Control, Technical Memorandum 33–669, Jet Propulsion Laboratory, February 1974.Google Scholar
  6. 6.
    J. Y. S. Luh, M. W. Walker, and R. P. C. Paul: Resolved-acceleration control of mechanical manipulators, IEEE Trans. on Automatic Control, Vol. 25, No. 3, pp. 468–474, June 1980.CrossRefGoogle Scholar
  7. 7.
    C. H. Wu, and R. P. Paul: Manipulator compliance based on joint torque control, Proc. 19th IEEE Conference on Decision and Control, Albuquerque, New Mexico, December 10–12, 1980.Google Scholar
  8. 8.
    J. M. Hollerbach: A recursive Lagrangian formulation of manipulator dynamics and a cooperative study of dynamics formulation complexity, IEEE Trans. on Systems, Man and Cybernetics, Vol. 10, No. 11, pp. 730–736, November 1980.CrossRefGoogle Scholar
  9. 9.
    K. K. D. Young: Controller design for a manipulator using theory of variable structure systems, IEEE Trans. on Systems, Man and Cybernetics, Vol. 8, No. 2, pp. 101–109, February 1978.CrossRefGoogle Scholar
  10. 10.
    S. Dubowsky and D. T. DesForges: The application of model-referenced adaptive control to robotic manipulators, Journal of Dynamic Systems, Measurement, and Control, Trans. of ASME, pp. 191–200, September 1979.Google Scholar
  11. 11.
    A. J. Koivo, and T.H. Guo: Control of robotic manipulator with adaptive controller, Proc. 20th IEEE Conference on Decision and Control, San Diego, California, pp. 271–276, December 16–18, 1981.Google Scholar
  12. 12.
    U. Borison: Self-tuning regulators for a class of multivariable systems, Automatica, Vol. 15, pp. 209–215, 1979.CrossRefGoogle Scholar
  13. 13.
    A Gelb, J. F. Kasper, et al.: Applied Optimal Estimation, MIT Press, Cambridge, MA, 1974.Google Scholar
  14. 14.
    K. J. Astrom, and P. Eykhoff: System identification—a survey, Automatica, Vol. 7, pp. 123–162, 1971.CrossRefGoogle Scholar
  15. 15.
    J. Y. S. Luh, and C. S. Lin: Approximate joint paths for control of mechanical manipulators, Proc. of IEEE Conference on Pattern Recognition and Image Processing, PRIP 82, Las Vegas, June 13–17, 1982.Google Scholar
  16. 16.
    J. Y. S. Luh, M. W. Walker, and R. P. C. Paul: On-line computational scheme for mechanical manipulators, Journal of Dynamic Systems, Measurement, and Control, Trans. of ASME, Vol. 102, No. 2 pp. 69–76, June 1980.CrossRefGoogle Scholar
  17. 17.
    A. E. Albert, and L. A. Gardner, Stochastic Approximation and Nonlinear Regression, MIT Press, Cambridge, MA, 1967.Google Scholar

Copyright information

© Crane Russak & Company Inc 1984

Authors and Affiliations

  • J. Y. S. Luh
    • 1
  1. 1.School of Electrical EngineeringPurdue UniversityWest LafayetteUSA

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