Open AccessIterative learning approach for traffic signal control of urban road networks
Author(s) -
Yan Fei,
Tian Fuli,
Shi Zhongke
Publication year - 2017
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/iet-cta.2016.0376
Subject(s) - iterative learning control , convergence (economics) , computer science , traffic flow (computer networking) , control theory (sociology) , signal (programming language) , iterative method , control engineering , control (management) , mathematical optimization , engineering , artificial intelligence , mathematics , algorithm , programming language , computer security , economics , economic growth
In this study, the authors apply iterative learning control (ILC) theory to address the urban traffic signal control problem in a macroscopic level traffic environment. The original urban traffic signal control problem is first formulated into an output regulating and disturbance rejection problem, and the ILC approach is applied to deal with this class of control problem using the repeatability feature of traffic flow. Then, the ILC strategy is further integrated with the traffic‐responsive urban control framework to cope with the random disturbances of traffic conditions in iteration cycle. Theoretical analysis shows that the proposed ILC‐based traffic signal control methods can guarantee the asymptotic convergence of the link occupancies to the desired ones. The main advantage of the proposed approaches is that they require less prior modelling knowledge in the control system design. The effectiveness of the proposed methods is further verified by extensive simulations.