Heterogeneous Traffic Management Using METANET Model with Filtered Feedback Linearization Control Approach
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- 文献・情報種別
- SAE Paper
No.2022-01-0090
- 掲載ページ
- 1-11(Total 11 p)
- 発行年月
- 2022年 3月
- 出版社
- SAE International
- 言語
- 英語
- イベント
- WCX SAE World Congress Experience 2022
書誌事項
著者(英) | 1) Pouria Karimi Shahri, 2) Amir H. Ghasemi |
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勤務先(英) | 1) University of North Carolina at Charlotte, 2) University of North Carolina at Charlotte |
抄録(英) | This paper presents a Filtered-Feedback-Linearization (FFL) controller for a non-signalized heterogeneous traffic network consisting of human-driven and autonomous vehicles at a macroscopic level. The FFL controller requires limited model information and it is effective for command following and rejection of unknown-and-unmeasured disturbances. This paper distinguishes between human-driven and autonomous vehicles in terms of their operational characteristics and controllability. To describe the traffic network’s behavior, we introduce an extended heterogeneous METANET model wherein the traffic flow, density, and velocity dynamics of each vehicle class are described. To develop traffic control policies, we propose a filtered-feedback-linearization control approach wherein the autonomous vehicles and human-driven vehicles are modeled as controllable agents. FFL controller sends the optimal suggested velocity of autonomous vehicles and human-driven vehicles as the controller command to the traffic system to reach the desired velocity, density, and average flow rate of each sub-network. Numerical simulation demonstrates the effectiveness of the proposed approach in managing the traffic flow of a heterogeneous traffic system. 翻訳 |