Intelligent Learning Controllers for Nonlinear Systems using Radial Basis Neural Networks

M. Arif, T. Ishihara, and H. Inooka

References

  1. [1] S. Arimoto, S. Kawamura, & F. Miyazaki, Bettering operationof robots by learning, Journal of Robotic Systems, 1 (2), 1984,123–140. doi:10.1002/rob.4620010203
  2. [2] S. Arimoto, S. Kawamura, & F. Miyazaki, Bettering operationof dynamic systems by learning: A new control theory forservomechanism or mechatronic systems, Proc. of the 23rdIEEE Conf. on Decision and Control, USA, 1984, 1064–1069.
  3. [3] C. Chien, A discrete iterative learning control of nonlinear time-varying systems. Proc. 35th Conf. on Decision and Control,Japan, 1996, 3056–3061. doi:10.1109/CDC.1996.573591
  4. [4] S.R. Oh, Z. Bien, & I.H. Suh, An iterative learning controlmethod with application for the robot manipulator, IEEEJournal of Robotics and Automation, 1988, 508–514. doi:10.1109/56.20435
  5. [5] Z. Bien, D.H. Hwang, & S.R. Oh, A nonlinear iterative learningmethod for robot path control, Robotica, 9, 1991, 387–392.
  6. [6] S.S. Saab, On the P-type learning control, IEEE Trans.Automatic Control, 39 (11), 1994, 2298–2302. doi:10.1109/9.333780
  7. [7] M. Arif, T. Ishihara, & H. Inooka, Incorporation of experiencein iterative learning controllers using locally weighted learning,Automatica, 37 (6), 2001, 881–888. doi:10.1016/S0005-1098(01)00030-9
  8. [8] J. Fu & N.K. Sinha, An iterative learning scheme for motioncontrol of robots using neural networks: A case study, Journalof Intelligent and Robotic Systems, 8, 1993, 375–398. doi:10.1007/BF01257950
  9. [9] W.S.T. Chow & Y. Fang, A recurrent neural network basedreal time learning control strategy applying to nonlinear systems with unknown dynamics, IEEE Trans. IE, 45 (1), 1998, 151–161. doi:10.1109/41.661316
  10. [10] S. Kawamura, F. Miyazaki, & S. Arimoto, A learning controlmethod for dynamical systems, Trans. SICE, 22 (1), 1986,56–62 (in Japanese).
  11. [11] M.J.D. Powell, Radial basis functions for multivariable interpolation: A review, Proc. IMA Conf. on Algorithms for theApproximation of Functions and Data, 1985, 143–167.
  12. [12] D.S. Broomhead & D. Lowe, Multivariable functional interpolation and adaptive networks, Complex Systems, 2, 1988, 321–355.
  13. [13] E. Hartman, J. Keeler, & J. Kowalski, Layered neural networkswith Gaussian hidden units as universal approximators, NeuralComputation, 2, 1990, 210–215. doi:10.1162/neco.1990.2.2.210
  14. [14] J. Park & I.W. Sandberg, Universal approximation and radial basis function networks, Neural Computation, 5, 1993, 305–316. doi:10.1162/neco.1993.5.2.305
  15. [15] H.N. Mhaskar & N. Hahm, Neural networks for functionalapproximation and system identification, Neural Computation,9, 1997, 143–159. doi:10.1162/neco.1997.9.1.143
  16. [16] F. Girosi, M. Jones, & T. Poggio, Regularization theory andneural network architectures, Neural Computation, 7, 1995,219-269. doi:10.1162/neco.1995.7.2.219

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