Data‐driven urban traffic model‐free adaptive iterative learning control with traffic data dropout compensation

Autor:	Dai Li, Zhongsheng Hou
Jazyk:	angličtina
Rok vydání:	2021
Předmět:	Interpolation and function approximation (numerical analysis) Other topics in statistics Control system analysis and synthesis methods Self-adjusting control systems Control engineering systems. Automatic machinery (General) TJ212-225
Zdroj:	IET Control Theory & Applications, Vol 15, Iss 11, Pp 1533-1544 (2021)
Druh dokumentu:	article
ISSN:	1751-8652 1751-8644
DOI:	10.1049/cth2.12141
Popis:	Abstract 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.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/5eb911921f29400594a07b0abf63ed60 Zobrazit plný text záznamu Plný text View record in DOAJ