MINIMUM PARAMETER LEARNING METHOD FOR AN N-LINK MANIPULATOR WITH NONLINEAR DISTURBANCE OBSERVER

Hongjun Yang and Jinkun Liu

References

  1. [1] Q. Zhou, P. Shi, S. Xu, and H. Li, Adaptive output feedback control for nonlinear time-delay systems by fuzzy approximation approach Fuzzy Systems, IEEE Transactions on, 21(2), 2013, 301–313.
  2. [2] B. Chen, X. Liu, K. Liu, and C. Lin, Direct adaptive fuzzy control of nonlinear strict-feedback systems, Automatica, 45(6), 2009, 1530–1535.
  3. [3] O. Kuljaca, R.R. Selmic, and J. Gadewadikar Adaptive neural network frequency control for thermopower generators power system, International Journal of Robotics & Automation, 26(1), 2011, 86–92.
  4. [4] H. Jafarian, M. Eghtesad, and A. Tavasoli, Combined adaptiverobust and neural network control of two RLED cooperating robots using backstepping design, International Journal of Robotics & Automation, 23(2), 2008, 106–116.
  5. [5] Z. Li, C. Yang, and J. Gu, Neuro-adaptive compliant force/motion control of uncertain constrained wheeled mobile manipulators, International Journal of Robotics & Automation, 22(3), 2007, 206–214.
  6. [6] J. Liang and L. Chen, Dynamics modeling for free-floating space-based robot during satellite capture and RBF neural network control for compound body stable movement, Acta Aeronautica et Astronautica Sinica, 34(4), 2013, 970–978.
  7. [7] H.J. Al-Gahtani and F.M. Mukhtar, RBF-based meshless method for the free vibration of beams on elastic foundations Applied Mathematics and Computation, 249, 2014, 198–208.
  8. [8] L. Wang, T. Chai, and C. Yang, Neural-network-based contouring control for robotic manipulators in operational space Control Systems Technology, IEEE Transactions on, 20(4), 2012, 1073–1080.
  9. [9] A. Mohammadi, M. Tavakoli, H.J. Marquez, and F. Hashemzadeh, Nonlinear disturbance observer design for robotic manipulators Control Engineering Practice, 21(3), 2013 253–267.
  10. [10] A. Yesidirek and F.L. Lewis, Feedback linearization using neural networks, Automatica, 31(11), 1995, 1659–1664.
  11. [11] F.L. Lewis, K. Liu, and A. Yesildirek, Neural net robot controller with guaranteed tracking performance, Neural Networks, IEEE Transactions on, 6(3), 1995, 703–715.
  12. [12] S.S. Ge, C.C. Hang, and L.C. Woon, Adaptive neural network control of robot manipulators in task space. Industrial Electronics, IEEE Transactions on, 44(6), 1997, 746–752.
  13. [13] J. Liu, Radial Basis Function (RBF) Neural Network Control for Mechanical Systems Design, Analysis and Matlab Simulation (Beijing: Tsinghua & Springer Press, 2013).
  14. [14] B. Chen, X. Liu, K. Liu, and C. Lin, Direct adaptive fuzzy control of nonlinear strict-feedback systems Automatica, 45(6), 2009 1530–1535.
  15. [15] P.M. Gahinet, A. Nemirovsky, A.J. Laub, and M. Chilali, LMI control toolbox: For use with MATLAB (Natick, MA: The Math Works, Inc 1995).
  16. [16] P.A. Ioannou and J. Sun, Robust adaptive control (Mineola, New York: Courier Dover Publications, 2012).
  17. [17] F.L. Lewis, C.T. Abdallab, and D.M. Dawson, Control of robot manipulators (New York, NY: Macmillan, 1993).
  18. [18] N. Kumar, V. Panwar, J.H. Borm, and J. Chai, Enhancing precision performance of trajectory tracking controller for robot manipulators using RBFNN and adaptive bound Applied Mathematics & Computation, 231(1), 2014, 320–328.

Important Links:

Go Back