System Development & Application
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App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 9
2775-2780

Iterative learning boundary control method for traffic subregion based on improved wolf pack algorithm

Jia Guangyao
Yan Fei
Zhang Tianyi
School of Electrical & Power Engineering, Taiyuan University of Technology, Taiyuan 030024, China
DOI: 10.19734/j.issn.1001-3695.2023.01.0023

Abstract

Aiming at the problems of slow convergence speed, too many iterations and poor control accuracy of traffic subregion boundary control method based on fixed gain iterative learning, this paper proposed a traffic subarea boundary control scheme based on iterative learning and improved wolf pack algorithm. In this scheme, it established the vehicle balance equation of traffic subarea network based on macroscopic fundamental diagram theory, and designed the iterative learning control law of the system. Secondly, it analyzed the influence of iterative learning control on the macroscopic fundamental diagram, and introduced the adaptive step size wolf pack algorithm to optimize the scale and differential gain coefficient of the iterative learning controller offline, and then put the optimal results into the next control cycle iterative learning control, so as to improve the convergence speed and accuracy. Finally, the convergence of the algorithm was proved mathematically, and the simulation results show that compared with the iterative learning controller with fixed gain, the proposed algorithm improves, the convergence speed and has better tracking accuracy of the expected trajectory of the system, and it has strong feasibility and effectiveness.

Foundation Support

国家自然科学基金资助项目(61703300)
中国博士后科学基金资助项目(2019M651082)
山西省应用基础研究项目(201801D221191)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.01.0023
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 9
Section: System Development & Application
Pages: 2775-2780
Serial Number: 1001-3695(2023)09-033-2775-06

Publish History

[2023-04-06] Accepted Paper
[2023-09-05] Printed Article

Cite This Article

贾光耀, 闫飞, 张添翼. 改进狼群算法的交通子区迭代学习边界控制方法 [J]. 计算机应用研究, 2023, 40 (9): 2775-2780. (Jia Guangyao, Yan Fei, Zhang Tianyi. Iterative learning boundary control method for traffic subregion based on improved wolf pack algorithm [J]. Application Research of Computers, 2023, 40 (9): 2775-2780. )

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  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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