Terminal iterative learning control based station stop control of a train. (English) Zbl 1230.93063

Summary: The Terminal Iterative Learning Control (TILC) method is introduced for the first time into the field of train station stop control and three TILC-based algorithms are proposed in this study. The TILC-based train station stop control approach utilizes the terminal stop position error in previous braking process to update the current control profile. The initial braking position, or the braking force, or their combination is chosen as the control input, and a corresponding learning law is developed. The terminal stop position error of each algorithm is guaranteed to converge to a small region related with the initial offset of braking position with rigorous analysis. The validity of the proposed algorithms is verified by illustrative numerical examples.


93C95 Application models in control theory
68T05 Learning and adaptive systems in artificial intelligence
93C15 Control/observation systems governed by ordinary differential equations
Full Text: DOI


[1] DOI: 10.1109/TSMCC.2007.905759
[2] DOI: 10.1002/rob.4620010203
[3] Davis Jr WJ, General Electric Review 29 pp 685– (1926)
[4] Goodwin GC, Adaptive Filtering Prediction and Control (1984)
[5] Hay WW, Railroad Engineering,, 2. ed. (1982)
[6] DOI: 10.1016/j.trc.2007.06.007
[7] DOI: 10.1109/TVT.2007.891431
[8] DOI: 10.1016/S0005-1098(96)00199-9 · Zbl 0882.93053
[9] DOI: 10.1016/S0376-0421(02)00029-5
[10] DOI: 10.1016/S0005-1098(99)00076-X · Zbl 0953.93508
[11] DOI: 10.1016/j.automatica.2008.05.017 · Zbl 1153.93380
[12] Zhang LP, ACTA Automatica Sinica 31 pp 309– (2005)
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.