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Journal of Control Science and Engineering
Volume 2017, Article ID 9269187, 7 pages
https://doi.org/10.1155/2017/9269187
Research Article

Iterative Learning Control with Forgetting Factor for Urban Road Network

1School of Automation, Northwestern Polytechnical University, Xi’an, Shaanxi 710072, China
2College of Information Engineering, Taiyuan University of Technology, Taiyuan, Shanxi 030024, China

Correspondence should be addressed to Fei Yan; moc.361@222041iefnay

Received 13 September 2016; Revised 10 January 2017; Accepted 8 February 2017; Published 28 February 2017

Academic Editor: Qiang Song

Copyright © 2017 Tianyi Lan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

In order to improve the traffic condition, a novel iterative learning control (ILC) algorithm with forgetting factor for urban road network is proposed by using the repeat characteristics of traffic flow in this paper. Rigorous analysis shows that the proposed ILC algorithm can guarantee the asymptotic convergence. Through iterative learning control of the traffic signals, the number of vehicles on each road in the network can gradually approach the desired level, thereby preventing oversaturation and traffic congestion. The introduced forgetting factor can effectively adjust the control input according to the states of the system and filter along the direction of the iteration. The results show that the forgetting factor has an important effect on the robustness of the system. The theoretical analysis and experimental simulations are given to verify the validity of the proposed method.