Iterative Reference Adjustment for High-Precision and Repetitive Motion Control Applications

IEEE Transactions on Control Systems Technology

Volumn 13, Issue 1, 2005, Pages 85-97

  Kiong Tan, Kok ^a   Zhao, Shao ^a   Huang, Sunan ^a  

^a NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

Author keywords

Iterative learning control (ILC); nonlinear systems; permanent magnet linear motors (PMLMs); precision motion control; radial basis function (RBF)

Indexed keywords

COMPUTER SIMULATION; ITERATIVE METHODS; LINEAR MOTORS; LYAPUNOV METHODS; MATHEMATICAL MODELS; MATRIX ALGEBRA; NONLINEAR CONTROL SYSTEMS; PERMANENT MAGNETS; RADIAL BASIS FUNCTION NETWORKS; THEOREM PROVING;

FEEDFORWARD CONTROL; ITERATIVE LEARNING CONTROL (ILC); PERMANENT MAGNET LINEAR MOTORS (PMLMS); PRECISION MOTION CONTROL;

MOTION CONTROL;

EID: 12344313322     PISSN: 10636536     EISSN: 15580865     Source Type: Journal    
DOI: 10.1109/TCST.2004.838549     Document Type: Article

References (16)

1. K. K. Tan, T. H. Lee, H. F. Dou, and S. Huang, Precision Motion Control Design and Implementation, ser. Advances in Industrial Control Series, New York: Springer-Verlag, 2001, ch. 2.
2. G. Otten, J. A. de Vries, J. van Amerongen, A. M. Rankers, and E. W. Gaal, “Linear motor motion control using a learning forward controller,” IEEE/ASME Trans. Mechatronics, vol. 2, no. 3, pp. 179–187, Sep. 1997.
3. T. H. Lee, K. K. Tan, S. Y. Lim, and H. F. Dou, “Iterative learning control of permanent-magnet linear motor with relay automatic tuning,” Mechatron., vol. 10, pp. 169–190, 2000.
4. J. Xiao, Q. Song, and D. Wang, “A learning control scheme based on neural networks for repeatable robot trajectory tracking,” in Proc. 1999 IEEE Int. Symp. Intelligent Control/Intelligent Systems Semiotics, pp. 102–107.
5. R. W. Longman, “Iterative learning and repetitive control for engineering practice,” Int. J. Control, vol. 73, no. 10, pp. 930–954, 2000.
6. K. Moore, “Iterative learning for trajectory control,” in Proc. 28th Conf. Decision and Control, 1989, pp. 860–865.
7. K. Hornik, M. Stinchcombe, and H. White, “Multilayer feedforward networks are universal approximators,” Neural Netw., vol. 2, pp. 359–366, 1989.
8. T. Holcomb and M. Morari, “Local training of radial basis function networks: Toward solving the hidden unit problem,” in Proc. Amer. Control Conf., 1991, pp. 2331–2336.
9. S. Haykin, Neural Networks a Comprehensive Foundation, 2nd ed. Englewood Cliffs, NJ: Prentice-Hall, 1999, p. 299.
10. D. Gorinevsky, “On the persistency of excitation in radial basis function network identification of nonlinear systems,” IEEE Trans. Neural Netw., vol. 6, no. 5, pp. 1237–1244, Sep. 1995.
11. K. Mitsuo, U. Yoji, I. Michiaki, and S. Ryoji, “Hierarchical neural network model for voluntary movement with application to robotics,” IEEE Control Syst. Mag., pp. 8–15, Apr. 1988.
12. S. Arimoto, S. Kawamura, and F. Miyazaki, “Bettering operation of robots by learning,” J. Robot. Syst., vol. 1, pp. 123–140, 1984.
13. H. F. Dou, Z. Y. Zhou, M. Sun, and Y. Chen, “Robust high-order P-type iterative learning control for a class of uncertain nonlinear systems,” in Proc. IEEE Int. Conf. Systems, Man, and Cybernetics, 1996, pp. 923–928.
14. S. N. Huang, K. K. Tan, and T. H. Lee, “Necessary and sufficient condition for convergence of iterative learning algorithm,” Automatica, vol. 38, no. 7, pp. 1257–1260, Jul. 2002.
15. C. T. Chen, Linear System Theory and Design, 3rd ed. New York: Oxford Univ. Press, 1999.
16. K. K. Tan, S. N. Huang, and T. H. Lee, “Robust Adaptive numerical compensation for friction and force ripple in permanent-magnet linear motors,” IEEE Trans. Magn., vol. 38, no. 1, pp. 221–228, Jan. 2002.