×

Incorporation of experience in iterative learning controllers using locally weighted learning. (English) Zbl 0987.93031

The authors aim to propose a new iterative learning control scheme. They wish to incorporate the experience that the iterative learning controller gains while tracking previous trajectories in order to track new desired ones. The planned advantage of their approach lies in reducing initial errors when the convergence rate is unchanged. Some ideas and particular examples of how to do this, together with numerical illustrations, are provided.

MSC:

93B51 Design techniques (robust design, computer-aided design, etc.)
93C41 Control/observation systems with incomplete information
68T05 Learning and adaptive systems in artificial intelligence
PDF BibTeX XML Cite
Full Text: DOI

References:

[1] Altman, N.S., An introduction to kernel and nearest neighbor nonparametric regression, The American Statistician, 46, 3, 175-185, (1992)
[2] Arimoto, S.; Kawamura, S.; Miyazaki, F., Bettering operation of robots by learning, Journal of robotic systems, 1, 2, 123-140, (1984)
[3] Arimoto, S., Kawamura, S., & Miyazaki, F. (1984b). Bettering operation of dynamic systems by learning: A new control theory for servomechanism or mechatronic systems. Proceedings of the 23rd IEEE conference on decision and control, USA (pp. 1064-1069).
[4] Atkeson, C.G.; Moore, A.W.; Schaal, S., Locally weighted learning, Artificial intelligence review, 11, 1, 11-73, (1997)
[5] Atkeson, C.G.; Moore, A.W.; Schaal, S., Locally weighted learning for control, Artificial intelligence review, 11, 1, 75-113, (1997)
[6] Bien, Z.; Hwang, D.H.; Oh, S.R., A nonlinear iterative learning method for robot path control, Robotica, 9, 387-392, (1991)
[7] Chien, C. (1996). A Discrete iterative learning control of nonlinear time-varying systems. Proceedings of the 35th conference on decision and control, Japan (pp. 3056-3061).
[8] Cleveland, W.S.; Devlin, S.J., Locally weighted regression: an approach to regression analysis by local Fitting, Journal of the American statistical association, 83, 596-610, (1988) · Zbl 1248.62054
[9] Kawamura, S., & Fukao, N. (1995). A time-scale interpolation for input torque patterns obtained through learning control on constrained robot motions. IEEE international conference on robotics and automation (pp. 2156-2161).
[10] Kawamura, S., Miyazaki, F., & Arimoto, S. (1987). Intelligent control of robot motion based on learning method. Proceedings of the IEEE international symposium, TH0178.
[11] Oh, S.R.; Bien, Z.; Suh, I.H., An iterative learning control method with application for the robot manipulator, IEEE journal of robotics and automation, 4, 5, 508-514, (1988)
[12] Phan, M.Q.; Juang, J.N., Designs of learning controllers based on autoregressive representation of a linear system, Journal of guidance, control and dynamics, 19, 2, 355-362, (1996) · Zbl 0848.93027
[13] Saab, S.S., On the P-type learning control, IEEE transactions of automatic control, 39, 11, 2298-2302, (1994) · Zbl 0825.93396
[14] Seidl, T., & Kriegel, H. (1998). Optimal multi-step k-nearest neighbor search. Proceedings of ACM SIGMOD international conference on management of data, USA.
[15] Wand, M.P.; Schucancy, W.R., Guassian-based kernels for curve estimation and window width selection, Canadian journal of statistics, 18, 249-260, (1990)
[16] Xu, J.X., Direct learning of control efforts for trajectories with different magnitude scales, Automatica, 33, 12, 2191-2195, (1997) · Zbl 0907.93032
[17] Xu, J.X., Direct learning of control efforts for trajectories with different time scales, IEEE transactions of automatic control, 43, 7, 1027-1030, (1998) · Zbl 0951.93051
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.