Open AccessData‐driven urban traffic model‐free adaptive iterative learning control with traffic data dropout compensation
Author(s) -
Li Dai,
Hou Zhongsheng
Publication year - 2021
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/cth2.12141
Subject(s) - traffic generation model , computer science , traffic flow (computer networking) , dropout (neural networks) , controller (irrigation) , iterative learning control , compensation (psychology) , adaptive control , floating car data , traffic congestion reconstruction with kerner's three phase theory , traffic congestion , real time computing , control (management) , artificial intelligence , engineering , machine learning , computer network , transport engineering , psychology , psychoanalysis , agronomy , biology
In this paper, to fully utilize the urban traffic flow characteristics of similarity and repeatability without using a mathematical traffic model, a data‐driven urban traffic control strategy based on model‐free adaptive iterative learning control (MFAILC) scheme is put forward. Firstly, by dynamically linearizing the urban traffic dynamics along the iteration axis, the traffic network system is transformed into a MFAILC data model with the help of repetitive pattern of urban traffic flow. Then, the traffic controller is designed based on the derived MFAILC data model only using the I/O data of the traffic network. Finally, a traffic data compensation method is proposed to deal with data dropout problem. Simulation study verifies the feasibility and effectiveness of the proposed control method.