Normalized Learning Rule for Iterative Learning Control
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The iterative learning control (ILC) is attractive for its simple structure, easy implementation. So the ILC is applied to various fields. But the unexpected huge overshoot can be observed as iteration repeat when we use the ILC to the real world applications. Such bad transient becomes an obstacle for using the ILC in the real field. Designers use a projection method to avoid the bad transient usually. However, the projection method does not show a good error performance enough. Therefore we propose a new learning rule to reduce such a bad transient effectively. The simple normalized learning rules for P-type and PD-type are presented and we prove their convergence. Numerical examples are given to show the effectiveness of the proposed learning control algorithms.
KeywordsHuge overshoot iterative learning nomalized learning rule P-type and PD-type control
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- J. X. Xu, R. Yan, and Y. Q. Chen, “On initial conditions in iterative learning control,” Proceedings of the American Control Conference, pp. 1349–1354, 2006.Google Scholar
- D. Y. Pi and K. Panaliappan, “Robustness of discrete nonlinear systems with open-closed loop iterative learning control,” Proc. of the 1st Int Conf on Machine Learning and Cybernetics, pp. 1263–1266, 2002.Google Scholar
- Z. Yang and C. W. Chan, “Perfect tracking of repetitive signals for a class of nonlinear systems,” Proceedings of the 17th World Congress IFAC, pp. 1490–1495, 2008. [click]Google Scholar
- G. Heinzinger, D. Fenwick, B. Paden, and F. Miyazaki, “Robust learning control,” Proc. of 28th IEEE Conf. on Decision and Control, pp. 436–440, 1989. [click]Google Scholar
- A. Madady, “PID type iterative learning control with optimal gains,” International Journal of Control, Automation, and Systems, vol. 6, no. 2, pp. 194–203, 2008.Google Scholar