A Repetitive Segmented Training Neural Network Controller with Applications to Robot Visual Servoing

P. Jiang and Y.Q. Chen

References

  1. [1] Y.D. Landau, Adaptive control: The model reference approach(New York: Marcel Dekker, 1979).
  2. [2] S. Sastry & A. Isidori, Adaptive control of linearizable system,IEEE Trans. Automatic Control, 34 (11), 1989, 1123–1131. doi:10.1109/9.40741
  3. [3] J.J.E. Slotine & S. Sastry, Tracking control of nonlinear systemusing sliding surface, with application to robot manipulators,International Journal of Control, 38 (2), 1983, 465–492.
  4. [4] M. Corless & G. Leitmann, Continuous state feedback guar-anteeing uniform ultimate boundedness for uncertain dynamicsystems, IEEE Trans. Automatic Control, 26 (5), 1981, 1139–1144. doi:10.1109/TAC.1981.1102785
  5. [5] R.M. Sanner & J.J.E. Slotine, Gaussian networks for directadaptive control, IEEE Trans. Neural Networks, 3 (6), 1992,837–863. doi:10.1109/72.165588
  6. [6] A. Yesildirek & F.L. Lewis, Feedback linearization using neuralnetworks, Automatica, 31 (11), 1995, 1659–1640. doi:10.1016/0005-1098(95)00078-B
  7. [7] S. Jagannathan, Discrete-time CMAC NN control of feedbacklinearizable nonlinear system under a persistence of excitation,IEEE. Trans. Neural Networks, 10 (1), 1999, 128–137. doi:10.1109/72.737499
  8. [8] Y.G. Leu, W.Y. Wang, & T.T. Lee, Robust adaptive fuzzy-neural controllers for uncertain nonlinear systems, IEEE Trans.Robotics and Automation, 15 (5), 1999, 805–817. doi:10.1109/70.795786
  9. [9] S. Arimoto, S. Kawamura, & F. Miyazaki, Bettering operationof robots by learning, Journal of Robotics System, 1 (2), 1984,123–140. doi:10.1002/rob.4620010203
  10. [10] S.S. Saab, On the P-type learning control, IEEE Trans.Automatic Control, 39 (11), 1994, 2298–2302. doi:10.1109/9.333780
  11. [11] Y. Chen, C. Wen, Z. Gong, & M. Sun, An iterative learningcontroller with initial state learning, IEEE Trans. AutomaticControl, 44 (2), 1999, 371–376. doi:10.1109/9.746269
  12. [12] G. Heizinger, D. Fenwick, B. Paden, & F. Miyazaki, Stabilityof learning control with disturbances and uncertain initialconditions, IEEE Trans. Automatic Control, 37 (1), 1992,110–114. doi:10.1109/9.109644
  13. [13] D. Wang, Convergence and robustness of discrete time nonlinearsystems with iterative learning control, Automatica, 34 (11),1998, 1445–1448. doi:10.1016/S0005-1098(98)00098-3
  14. [14] Ping Jiang, Chen Huitang, Wang Yuejuan, Stability analysis foriterative learning control and its application to manipulators,ACTA Automatica Sinica, 23 (4), 1997, 462-467.
  15. [15] J. Xu & Z. Qu, Robust iterative learning control for a class ofnonlinear systems, Automatica, 34(8), 1998, 983–988.220 doi:10.1016/S0005-1098(98)00036-3
  16. [16] M.M. Polycarpou, Stable adaptive neural control scheme fornonlinear systems, IEEE Trans. Automatic Control, 41 (3),1996, 447–451. doi:10.1109/9.486648
  17. [17] G.P. Liu, V. Kadirkamanathan, & S.A. Billings, Variableneural networks for adaptive control of nonlinear systems,IEEE Trans. Systems, Man, and Cybernetics, Part C, 29 (1),1999, 34–43. doi:10.1109/5326.740668
  18. [18] E.B. Kosmatopoulos, M.M. Polycarpou, M.A. Christodoulous,& P.A. Ioannou, High-order neural network structures for iden-tification of dynamical systems, IEEE Trans. Neural Networks,6 (2), 1995, 422–431. doi:10.1109/72.363477
  19. [19] P. Jiang & R. Unbehauen, Robot visual servoing with iterativelearning control, IEEE Trans. on System, Man, and Cybernet-ics, Part A: System and Humans, 32 (2), 2002, 281–287. doi:10.1109/TSMCA.2002.1021116
  20. [20] J.J.E. Slotine & W. Li, Applied nonlinear control (EnglewoodCliffs, NJ: Prentice-Hall, 1991).
  21. [21] H. Hashimoto, T. Kubota, M. Kudou, & F. Harashima, Self-organizing visual servo system based on neural networks, IEEEControl Systems Magazine, 12 (2), 1992, 31–36. doi:10.1109/37.126850
  22. [22] J.A. Walter & K.J. Schulten, Implementation of self-organizingneural networks for visuo-motor control of an industrial robot,IEEE Trans. on Neural Networks, 4 (1), 1993, 86–95. doi:10.1109/72.182698
  23. [23] J.T. Feddema & C.S.G. Lee, Adaptive image prediction andcontrol for visual tracking with a hand-eye coordinated camera,IEEE Trans. Systems, Man, and Cybernetics, 20 (5), 1990,1172–1183. doi:10.1109/21.59979
  24. [24] N.P. Papanikolopoulos & P.K. Khosla, Adaptive robot visualtracking theory and experiments, IEEE Trans. AutomaticControl, 38 (3), 1993, 429–445. doi:10.1109/9.210141
  25. [25] N.P. Papanikolopoulos, B.J. Nelson, & P.K. Khosla, Six degreeof freedom hand/eye visual tracking with uncertain parameters,IEEE Trans. Robotics and Automation, 11 (5), 1995, 725–732. doi:10.1109/70.466612
  26. [26] K. Hosoda & M. Asada, Versatile visual servoing withoutknowledge of true Jacobian, Proc. IEEE/RSJ Int. Conf. onIntelligent Robots and Systems, Munich, Germany, 1994, 186–193. doi:10.1109/IROS.1994.407392
  27. [27] J.A. Piepmeier, G.V. McMurray, H. Lipkin, A dynamic quasi-Newton method for uncalibrated visual servoing, Proc. Int.Conf. on Robotics and Automation, Minneapolis, MN, USA,1999, 1595–1600.
  28. [28] J.A. Piepmeier, B.A. Gumpert, & H. Lipkin, Uncalibrationeye-in-hand visual servoing, Proc. IEEE Int. Conf. on Roboticsand Automation, Washington, DC, 2002, 568–573. doi:10.1109/ROBOT.2002.1013419
  29. [29] J.S. Albus, A new approach to manipulator control: The cere-bellar model articulation controller (CMAC), Trans. ASME,Journal of Dynamics, Systems, Measurement, and Control,97, 1975, 220–227.

Important Links:

Go Back