Iterative learning identification method for parameters of macro traffic flow model

Issue title: Meta-Heuristic Techniques for Solving Computational Engineering Problems: Challenges and New Research Directions

Guest editors: Suresh Chandra Satapathy, Rashmi Agrawal and Vicente García Díaz

Article type: Research Article

Authors: Chen, Yuan^{; *}

Affiliations: Hunan Communication Polytechnic, Hunan, China

Correspondence: [*] Corresponding author. Yuan Chen, Hunan Communication Polytechnic, Hunan, China. E-mail: [email protected].

Abstract: The macro traffic flow model of expressway is transformed into a general discrete-time nonlinear system model including this model by using the repeatability of the macro traffic flow model, and then a parameter identification algorithm based on iterative learning is designed for this general discrete-time nonlinear system model. The convergence of this parameter identification scheme is proved by strict theoretical derivation and robustness. Simulation results verify the effectiveness of the algorithm.

Keywords: Macro traffic flow model, iterative learning control, parameter identification

DOI: 10.3233/JIFS-189466

Journal: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 6295-6303, 2021