International Journal of Control, Automation, and Systems

Institute of Control, Robotics and Systems (제어로봇시스템학회)
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A Direct Adaptive Fuzzy Control of Nonlinear Systems with Application to Robot Manipulator Tracking Control

  • Cho, Young-Wan (Dept. of Computer Engineering, Seokyeong University) ;
  • Seo, Ki-Sung (Dept. of Electronic Engineering, Seokyeong University) ;
  • Lee, Hee-Jin (Dept. of Information and Control Engineering, Hankyong University)
  • Published : 2007.12.31
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Abstract

In this paper, we propose a direct model reference adaptive fuzzy control (MRAFC) for MIMO nonlinear systems whose structure is represented by the Takagi-Sugeno fuzzy model. The adaptive law of the MRAFC estimates the approximation error of the fuzzy logic system so that it provides asymptotic tracking of the reference signal for the systems with uncertain or slowly time-varying parameters. The developed control law and adaptive law guarantee the boundedness of all signals in the closed-loop system. In addition, the plant state tracks the state of the reference model asymptotically with time for any bounded reference input signal. To verify the validity and effectiveness of the MRAFC scheme, the suggested analysis and design techniques are applied to the tracking control of robot manipulator and simulation studies are carried out. In the control design, the MRAFC is combined with feedforward PD control to make the actual joint trajectories of the robot manipulator with system uncertainties track the desired reference joint position trajectories asymptotically stably.

Keywords

References

  1. K. Astrom and B. Wittenmark, Adaptive Control, Addison Wesley, pp. 343-359, 1988
  2. P. A. Ioannou and J. Sun, Robust Adaptive Control, Prentice Hall International Editions, 1996
  3. J. J. Slotine and W. Li, Applied Nonlinear Control, Prentice-Hall, 1991
  4. J. J. Slotine and S. S. Sastry, 'Tracking control of nonlinear systems using sliding surfaces, with application to robot manipulators,' International Journal of Control, vol. 38, no. 2, pp. 465-492, 1983 https://doi.org/10.1080/00207178308933088
  5. T. C. Hsia, 'Adaptive control of robot manipulators-A review,' Proc. of IEEE Int. Conf. Robotics and Automation, San Francisco, vol. 3, pp. 183-189, 1986
  6. H. K. Khalil, 'Adaptive output feedback control of nonlinear systems represented by input-output models,' IEEE Trans. on Automatic Control, vol. 41, no. 2, pp. 177-188, 1996 https://doi.org/10.1109/9.481517
  7. Z. Qu, 'Adaptive and robust controllers of uncertain systems with nonlinear parameterization,' IEEE Trans. on Automatic Control, vol. 48, no. 10, 1817-1823, 2003 https://doi.org/10.1109/TAC.2003.817931
  8. T. Takagi and M. Sugeno, 'Fuzzy identification of systems and its applications to modeling and control,' IEEE Trans. on Systems, Man, and Cybernetics, vol. 15, no. 1, pp. 116-132, 1985
  9. K. Tanaka and M. Sugeno, 'Stability analysis and design of fuzzy control systems,' Fuzzy Sets and Systems, vol. 45, no. 2, pp. 135-156, 1992 https://doi.org/10.1016/0165-0114(92)90113-I
  10. K. Tanaka and M. Sano, 'A robust stabilization problem of fuzzy control systems and its application to backing up control of a trucktrailer,' IEEE Trans. on Fuzzy Systems, vol. 2, no. 2, pp. 119-133, 1994 https://doi.org/10.1109/91.277961
  11. H. O. Wang, K. Tanaka, and M. F. Griffin, 'An approach to fuzzy control of nonlinear systems: Stability and design issues,' IEEE Trans. on Fuzzy Systems, vol. 4, no. 1, pp. 14-23, 1996 https://doi.org/10.1109/91.481841
  12. C. S. Ting, T. H. S. Li, and F. C. Kung, 'An approach to systematic design of fuzzy control system,' Fuzzy Sets and Systems, vol. 77, pp. 151-166, 1996 https://doi.org/10.1016/0165-0114(95)00075-5
  13. L.-X. Wang, A Course in Fuzzy Systems and Control, Prentice-Hall International, Inc., 1997
  14. J. T. Spooner and K. M. Passino, 'Stable adaptive control using fuzzy systems and neural networks,' IEEE Trans. on Fuzzy Systems, vol. 4, no. 3, pp. 339-359, 1996 https://doi.org/10.1109/91.531775
  15. D.-L. Tsay, H.-Y. Chung, and C.-J. Lee, 'The adaptive control of nonlinear systems using the Sugeno-type of fuzzy logic,' IEEE Trans. on Fuzzy Systems, vol. 7, no. 2, pp. 225-229, 1999 https://doi.org/10.1109/91.755402
  16. C. W. Park, and Y. W. Cho, 'T-S model based indirect adaptive fuzzy control using online parameter estimation,' IEEE Trans. on Sys., Man, and Cybern.-Part B, vol. 34, no. 6, pp. 2293-2302, 2004 https://doi.org/10.1109/TSMCB.2004.835079
  17. H. Chekireb, M. Tadjine, and D. Bouchaffra, 'Direct adaptive fuzzy control of nonlinear system class with applications,' Control and Intelligent Systems, vol. 31 no. 2, pp. 113-121, 2003
  18. H. Schulte, 'Model-based fuzzy tracking control of two-link manipulators with dual actuators,' Proc. of the 2nd IFAC Workshop on Advanced Fuzzy-Neural Control, 2004
  19. A. Bonarini, A. Danieli, and M. Danieli, 'Adaptive fuzzy model-following control: A robotic manipulator application,' Proc. EUFIT '94, ELITE Foundation, Aachen, Germany, pp. 13-18, 1994
  20. I. Skrjanc, S. Blazic, and D. Matko, 'Modelreference fuzzy adaptive control as a framework for nonlinear system control,' Journal of Intelligent and Robotic Systems, vol. 36, no. 3, pp. 331-347, 2003 https://doi.org/10.1023/A:1023089005353
  21. X.-J. Ma and Z.-Q. Sun, 'Output tracking and regulation of nonlinear system based on Takagi- Sugeno fuzzy model,' IEEE Trans. on Syst. Man Cyber., Part B, vol. 30, no. 1, pp. 47-59, 2000 https://doi.org/10.1109/3477.826946
  22. R. Wai and P. Chen, 'Intelligent tracking control for robot manipulator including actuator dynamics via TSK-type fuzzy neural network,' IEEE Trans. on Fuzzy Systems, vol. 12, no. 4, pp. 552-560, 2004 https://doi.org/10.1109/TFUZZ.2004.832531