×

zbMATH — the first resource for mathematics

Pseudo-downsampled iterative learning control. (English) Zbl 1284.93153
Summary: In this paper, a simple and effective multirate iterative learning control (ILC), referred as pseudo-downsampled ILC, is proposed to deal with initial state error. This scheme downsamples the tracking error and input signals collected from the feedback control system before they are used in the ILC learning law. The output of the ILC is interpolated to generate the input for the next cycle. Analysis shows that the exponential decay of the tracking error can be expected and convergence condition can be ensured by downsampling. Other advantages of the proposed pseudo-downsampled ILC include no need for a filter design and reduction of memory size and computation. Experimental results demonstrate the effectiveness of the proposed scheme.

MSC:
93C57 Sampled-data control/observation systems
68T05 Learning and adaptive systems in artificial intelligence
93B35 Sensitivity (robustness)
PDF BibTeX XML Cite
Full Text: DOI
References:
[1] Lee, Automatica 33 pp 1591– (1997)
[2] Hillenbrand, International Journal of Control 73 pp 882– (2000)
[3] An observation about monotonic convergence of discrete-time, P-type iterative learning control. IEEE Symposium on Intelligence Control, Mexico, 2001; 45–49.
[4] Longman, International Journal of Control 73 pp 930– (2000)
[5] Arimoto, Journal of Robotic System 1 pp 123– (1984)
[6] Lee, IEE Proceedings–D 138 pp 525– (1991)
[7] , . Cutoff-frequency phase-in ILC to overcome initial position offsets. Proceedings of the IEEE International Conference on Control Applications, Taiwan, 2004; 983–988.
[8] Chang, Advances in Astronautical Sciences 76 pp 2035– (1992)
[9] Park, Asian Journal of Control 4 pp 111– (2002)
[10] Zhang, International Journal of Control 80 pp 349– (2007)
[11] , . Stable iterative learning control using cubic splines. Proceedings of the UKACC International Control Conference, Glasgow, Scotland, U.K., August 2006.
[12] Ratcliffe, International Journal of Adaptive Control and Signal Processing 19 pp 769– (2005)
[13] Ratcliffe, International Journal of Adaptive Control and Signal Processing 21 pp 300– (2007)
[14] Heinzinger, IEEE Transactions on Automatic Control 37 pp 110– (1992)
[15] , . Robustness of P-type learning control with a forgetting factor for robot motions. Proceedings of the 29th Conference on Decision and Control, Honolulu, HI, U.S.A., December 1990; 2640–2645.
[16] Saab, IEEE Transactions on Automatic Control 39 pp 2298– (1994)
[17] Wang, Automatica 34 pp 1445– (1998)
[18] Wang, International Journal of Control 73 pp 890– (2000)
[19] Sun, Automatica 38 pp 1177– (2002)
[20] , , . Initial state learning method for iterative learning control of uncertain time-varying systems. Proceedings of the 35th IEEE Conference on Decision and Control, vol. 4, Kobe, Japan, December 1996; 3996–4001.
[21] Chen, IEEE Transactions on Automatic Control 44 pp 371– (1999)
[22] Kuc, Automatica 28 pp 1215– (1992)
[23] Iterative learning control–convergence using high gain feedback. Proceedings of the Conference on Decision and Control, Arizona, 1992; 2515–2546.
[24] Lee, International Journal of Control 64 pp 345– (1997)
[25] Park, International Journal of Control 73 pp 871– (2000)
[26] , , , . Experiments in the use of learning control for maximum precision robot trajectory tracking. in Proceedings on Information Science and Systems, NJ, U.S.A., 1994; 951–958.
[27] Sadegh, Journal of Dynamic Systems, Measurement, and Control–Transactions of the ASME 124 pp 668– (2002)
[28] . Automated tuning concepts for iterative learning and repetitive control laws. Proceedings of the 37th CDC, FL, U.S.A., 1998; 192–198.
[29] Zhang, IEEE Transactions on System, Man, and Cybernetics, Part B 35 pp 107– (2005)
[30] Tomizuka, Journal of Dynamic Systems, Measurement, and Control 109 pp 65– (1987)
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.