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Application of Fuzzy Logic for the Solution of Inverse Kinematics and Hierarchical Controls of Robotic Manipulators

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Abstract

In this paper, hierarchical control techniques is used for controlling a robotic manipulator. The proposed method is based on the establishment of a non-linear mapping between Cartesian and joint coordinates using fuzzy logic in order to direct each individual joint. The hierarchical control will be implemented with fuzzy logic to improve the robustness and reduce the run time computational requirements. Hierarchical control consists of solving the inverse kinematic equations using fuzzy logic to direct each individual joint. A commercial Microbot with three degrees of freedom is utilized to evaluate this methodology. A decentralized fuzzy controller is used for each joint, with a Fuzzy Associative Memories (FAM) performing the inverse kinematic mapping in a supervisory mode. The FAM determines the inverse kinematic mapping which maps the desired Cartesian coordinates to the individual joint angles. The individual fuzzy controller for each joint generates the required control signal to a DC motor to move the associated link to the new position. The proposed hierarchical fuzzy controller is compared to a conventional controller. The simulation experiments indeed demonstate the effectiveness of the proposed method.

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References

  1. Fu, F. S., Gonzalez, R. C., and Lee, C. S. G.: Robotics Control, Sensing, Vision, and Intelligence, McGraw-Hill, 1987.

  2. Wolovich, W. A.: Robotics: Basic Analysis and Design, Holt, Rinehart and Winston, 1987.

  3. Ross, T. J.: Fuzzy Logic with Engineering Applications, McGraw-Hill, 1995.

  4. Kumbla, K. K. and Jamshidi, M.: Control of robotic manipulator using fuzzy logic, in: IEEE Internat. Conf. on Fuzzy Logic, 1994.

  5. Lea, R. N., Hoblit, J., and Yashvant, J.: Fuzzy logic based robotic arm control, in: R. Mark (ed.), Fuzzy Logic Technology and Application, 1994.

  6. Moudgal, V. G., Kwong, W. A., Passino, K. M., and Yurkovich, S.: Fuzzy learning control for a flexible-link robot, IEEE Trans. Fuzzy Systems 3(2)(1995).

  7. Martinez, J., Bowles, J., and Mills, P.: A fuzzy logic positioning system for an articulated robot arm, in: IEEE International Conference on Fuzzy Systems, 1996.

  8. Shieh, M. and Li, T. S.: Implementation of integrated fuzzy logic controller for servomotor system, in: IEEE Robotic Conference, 1995.

  9. Nedungadi, A.: Application of fuzzy logic to solve the robot inverse kinematic problem, in: Society of Manufacturing Engineers 4th World Conf. on Robotic Research, 1991.

  10. Li, Y. F. and Lau, C. C.: Application of fuzzy control for servo systems, in: IEEE, 1988.

  11. Li, Y. F. and Lau, C. C.: Development of fuzzy algorithms for servo systems, in: IEEE Internat. Conf. on Robotics and Automation, 1988.

  12. Lim, C. M. and Hiyama, T.: Application of fuzzy logic control to a manipulator, IEEE Trans. on Robotics and Automation 7(5)(1991).

  13. Nianzui, Z., Ruhui, Z., and Maoji, F.: Fuzzy control used in robotic arm position control, in: IEEE Internat. Conf. on Fuzzy Systems, 1994.

  14. Jang, J. R.: Self-learning fuzzy controllers based on temporal back propagation, IEEE Trans. on Neural Networks 3(5)(1992).

  15. Fuzzy Logic Toolbox User's Guide, MathWorks, Inc, 1995.

  16. Wrigt, R.: Can machine think?, Time Magazine 147(13) (1996).

  17. Antsaklis, P. J. and Passino, K. M. (eds): An Introduction to Intelligent and Autonomous Control, Kluwer Academic, Norwell, MA, 1993.

    Google Scholar 

  18. Zilouchian, A., Hamono, F., and Jordnidis, T.: Recent trend and industrial applications of intelligent control system using artificial neural networks and fuzzy logic, in: S. Tzafestas(ed.), Method and Application of Intelligent Control, Kluwer Academic, 1997.

  19. Zadeh, L. A.: Making the computers think like people, IEEE Spectrum (1994).

  20. Zadeh, L. A.: A critical view of our research in automatic control, IRE Trans. on Automatic Controls AC-7(1962), 74.

    Google Scholar 

  21. Zadeh, L. A.: Fuzzy sets, Information and Control 8(1965), 338–353.

    Google Scholar 

  22. Mamdani, E. H.: Application of fuzzy algorithms for control of simple dynamic plant, Proceeding of IEE 121(12)(1974).

  23. Yager, R. and Zadeh, L. A. (eds): An Introduction to Fuzzy Logic Applications in Intelligent Systems, Kluwer Academic, Boston, 1992.

    Google Scholar 

  24. Jang, J., Sun, C., and Mizutani, E.: Neuro Fuzzy and Soft Computing, Printice-Hall, Upper Saddle River, NJ, 1997.

    Google Scholar 

  25. Surgeno, M. (ed.): Industrial Applications of Fuzzy Control, North-Holland, Amsterdam, 1985.

    Google Scholar 

  26. Gupta, M. and Sinha, N. (eds): Intelligent Control Systems: Theory and Applications, IEEE Press, Picataway, NJ, 1996.

    Google Scholar 

  27. Marks II, R. (ed.): Fuzzy Logic Technology and Applications, IEEE Press, Picataway, NJ, 1994.

    Google Scholar 

  28. Lime, C. M. and Hiyama, T.: Application of fuzzy control to a manipulator, IEEE Trans. On Robotics and Automation 7(5) (1991).

  29. Nedungadi, A.: Application of fuzzy logic to solve the robot inverse kinematics problem, in: Proceeding of 4th World Conf. on Robotics Research, 13, 1991, pp. 1–14.

    Google Scholar 

  30. Nguyen, L., Patel, R., and Khorasani, K.: Neural network architectures for the forward kinematic problem in robotics, in: Proceeding of IEEE Int. Conf. on Neural Networks, 1990, pp. 393–399.

  31. Liu, H. Iberall, T., and Bekey, G.: Neural network architecture for robot hand control, IEEE Control Systems 9(3) (1989), 38–43.

    Google Scholar 

  32. Eckmiller, R.: Neural nets for sensory and motor trajectories, IEEE Control Systems 9(3) (1989), 53–59.

    Google Scholar 

  33. Nagata, S., Sekiguchi, M., and Asakewa, K.: Mobil robot control by a structured hierarchical neural network, IEEE Control Systems Magazine 10(3) (1990), 69–76.

    Google Scholar 

  34. Handelman, D., Lane, S., and Gelfand, J.: Integrating neural networks and knowledge-based systems for intelligent robotic control, IEEE Control Systems Magazine 10(3) (1990), 77—86.

    Google Scholar 

  35. Rabelo, L. C. and Avula, X.: Hierarchical neurocontroller architecture for robotic manipulation, IEEE Control Systems Magazine 12(2) (1992), 37–41.

    Google Scholar 

  36. Wang, D. and Zilouchian, A.: Solution of kinematic's of robot manipulators using a Kohonen self organization neural network, in: Proceeding of 1997 IEEE Internat. Symp. on Intelligent Control.

  37. Jang, J. S. and Sun, C.: Neuro-fuzzy modeling and control, Proceedings of IEEE 83(3) (1995), 378–406.

    Google Scholar 

  38. Kosko, B.: Neural Networks and Fuzzy Systems, Prentice-Hall, Englewood Cliffs, NJ, 1991.

    Google Scholar 

  39. Kosko, B.: Fuzzy Engineering, Printice-Hall, Upper Saddle River, NJ, 1997.

    Google Scholar 

  40. Zimmermann, H.: Fuzzy Set Theory and its Applications, Kluwer Academic, Boston, (1991).

    Google Scholar 

  41. Nguyen, H., Sugeno, M., Tong, R., and Yager, R.: Theoretical Aspects of Fuzzy Control,Wiley, New York, 1995.

    Google Scholar 

  42. Ralescu, A. (ed.): Applied Research in Fuzzy Technology, Kluwer Academic, Boston, 1994.

    Google Scholar 

  43. Kaufmann, A. and Gupta, M. (eds): Introduction to Fuzzy Arithmetic Theory and Applications, Van Nostrand Reinhold, New York, 1985.

    Google Scholar 

  44. Lewis, F. L., Abdallah, C. T., and Dawson, D. M.: Control of Robot Manipulator, Macmillan, New York, 1993.

    Google Scholar 

  45. Slotin, J. J. and Li, W.: Applied Nonlinear Control, Prentice-Hall, 1991.

  46. Bailey, E. and Arapostathis: Simple sliding mode control scheme applied to robot manipulators, Internat. J. Control 45(4) (1987), 1197–1209.

    Google Scholar 

  47. Howard, D.: Application of fuzzy logic for the solution of inverse kinematic and hierarchical controls of robotic manipulators, M.S. Thesis, Florida Atlantic University, May 1997.

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Authors and Affiliations

  1. Department of Electrical Engineering, Florida Atlantic University, Boca Raton, FL, 33431, U.S.A.

    David W. Howard & Ali Zilouchian

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  1. David W. Howard
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  2. Ali Zilouchian
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Howard, D.W., Zilouchian, A. Application of Fuzzy Logic for the Solution of Inverse Kinematics and Hierarchical Controls of Robotic Manipulators. Journal of Intelligent and Robotic Systems 23, 217–247 (1998). https://doi.org/10.1023/A:1007907528825

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