A Multi-vehicle Cooperative Control Algorithm Based on Data-Driven Adaptive Control Strategy for Heterogeneous Human-driven and Autonomous Vehicles

WANG Huixian; LI Bo; ZHENG Hongjiang; CHEN Wei

doi:10.3963/j.jssn.1674-4861.2021.01.009

Volume 39 Issue 1

Feb. 2021

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Article Navigation > Journal of Transport Information and Safety > 2021 > 39(1): 76-86

WANG Huixian, LI Bo, ZHENG Hongjiang, CHEN Wei. A Multi-vehicle Cooperative Control Algorithm Based on Data-Driven Adaptive Control Strategy for Heterogeneous Human-driven and Autonomous Vehicles[J]. Journal of Transport Information and Safety, 2021, 39(1): 76-86. doi: 10.3963/j.jssn.1674-4861.2021.01.009

Citation:

WANG Huixian, LI Bo, ZHENG Hongjiang, CHEN Wei. A Multi-vehicle Cooperative Control Algorithm Based on Data-Driven Adaptive Control Strategy for Heterogeneous Human-driven and Autonomous Vehicles[J]. Journal of Transport Information and Safety, 2021, 39(1): 76-86. doi: 10.3963/j.jssn.1674-4861.2021.01.009

Citation:

WANG Huixian, LI Bo, ZHENG Hongjiang, CHEN Wei. A Multi-vehicle Cooperative Control Algorithm Based on Data-Driven Adaptive Control Strategy for Heterogeneous Human-driven and Autonomous Vehicles[J]. Journal of Transport Information and Safety, 2021, 39(1): 76-86. doi: 10.3963/j.jssn.1674-4861.2021.01.009

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A Multi-vehicle Cooperative Control Algorithm Based on Data-Driven Adaptive Control Strategy for Heterogeneous Human-driven and Autonomous Vehicles

doi: 10.3963/j.jssn.1674-4861.2021.01.009

WANG Huixian^1
,,
LI Bo^{2, 3},
ZHENG Hongjiang^{2, 3},
CHEN Wei^{4
,
,}

1.
School of Information Management, Shanghai Lixin University of Accounting and Finance, Shanghai 201209, China
2.
Department of Technology, Shanghai Engineering Technology Research Center for Intelligent and Connected Vehicle Terminals, Shanghai 200030, China
3.
Department of Technology, Shanghai PATEO Electronic Equipment Manufacturing Limited Company, Shanghai 200030, China
4.
School of Automation, Wuhan University of Technology, Wuhan 430070, China

Received Date: 2020-07-23
Publish Date: 2021-02-28

Abstract

Abstract

Traditional model-based control methods need to obtain parameters of driving behaviors of drivers and system dynamics of vehicles in a multi-vehicle cooperative control system. However, these parameters cannot be obtained accurately in actual transport systems. A data-driven adaptive dynamic programming control algorithm is proposed to solve the problem. Under the environment of mixed manned and unmanned vehicles, the horizontal and vertical control models of the multi-vehicle cooperative control system are analyzed to derive its state equation. A recursive numerical method is used to approximate an optimal solution. Optimal control inputs are obtained by optimizing a feedback control matrix. The proposed algorithm simplifies control input parameters of the system. Besides, the optimal control of unmanned vehicles can be realized only using two parameters of basic safety messages of vehicles in real-time as the controller's inputs: steering the angel of the fore wheel and expected longitudinal acceleration. A co-simulation is conducted based on CarSim and Simulink. The results show that the proposed algorithm has simple control parameters, fast convergence speed, high control accuracy, and strong adaptability. It ensures the stability of the multi-vehicle cooperative control system and controls unmanned vehicles in platooning to maintain the desired velocity and desired heading. Moreover, its lateral error between the actual trajectory and expected trajectory tends to zero during driving on the road with arbitrary curvature.
- intelligent transportation,
- multi-vehicle collaboration control,
- data-driven control,
- adaptive dynamic pro-gramming,
- V2X communication,
- human-driven and autonomous vehicles

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A Multi-vehicle Cooperative Control Algorithm Based on Data-Driven Adaptive Control Strategy for Heterogeneous Human-driven and Autonomous Vehicles

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