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Vehicle Platoon Tracking Control Based on Adaptive Neural Network Algorithm

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  • Robot and Applications
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Abstract

To improve the tracking efficiency of the platoon during driving and ensure the spacing safety between vehicles, a platoon tracking control strategy based on the adaptive neural network algorithm is developed. In this method, the nonlinear term in the vehicle model is estimated by the adaptive neural network, and the estimated value is used to compensate for the control input and enhance the tracking performance of the vehicle platoon. In addition, the estimation update law of target trajectory and adjacent vehicle acceleration is designed through the adaptive method, which relaxes the trajectory generation requirements of virtual vehicles, improves the tracking performance of vehicle platoon, reduces the measurement and communication burden in the platoon, and ensures the security and stability of vehicle platoon system. After constructing the vehicle and desired path model, the control objective is formulated, and the adaptive neural network algorithm controller is designed. Meanwhile, the stability of the controller is verified by the Lyapunov method. The feasibility of the proposed method is proved by simulation and experiment. Rigorous theoretical derivation and experiments confirm that the proposed strategy has obvious advantages over other existing strategies.

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Authors and Affiliations

  1. College of Electrical Engineering and Automation, Fuzhou University, Fuzhou, 350108, China

    Jie Huang, Jianfei Chen, Hongsheng Yang & Dongfang Li

  2. 5G+ Industrial Internet Institute, Fuzhou University, Fuzhou, 350108, China

    Jie Huang, Jianfei Chen, Hongsheng Yang & Dongfang Li

Authors
  1. Jie Huang
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  2. Jianfei Chen
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  3. Hongsheng Yang
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  4. Dongfang Li
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Correspondence to Dongfang Li.

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This work was supported in part by the National Natural Science Foundation of China under Grants 61603094.

Jie Huang received his B.E. degree in electrical engineering and automation, an M.E. degree in control engineering from Fuzhou University, China, in 2005 and 2010, respectively, and a Ph.D. degree in control science and engineering from Bei**g Institute of Technology, Bei**g, China in 2015. From 2005 to 2015, he was a lecturer with Fujian Institute of Education, Fuzhou, China. From 2014 to 2016, he was postdoctoral researcher with the Faculty of Mathematics and Natural Sciences, University of Groningen, the Netherlands. From 2016 to 2018, he held lecturer appointments with the Faculty of Science and Engineering, University of Groningen, the Netherlands. He is currently a full professor of robotic and control with the College of Electrical Engineering and Automation, Fuzhou University, China and the director of the 5G+ Industrial Internet Institute, Fuzhou University, China. He is the vice-president of the Fujian Automation Association, Fujian Province, China. His research interests include autonomous robots, complex network dynamics, and multi-agent systems.

Jianfei Chen received his B.E. degree in measurement and control technology and instrumentation from Qingdao University of Science and Technology, Shandong, China, in 2020. He is now an M.E. student at Fuzhou University, Fuzhou, China. His research interests include multi-agent systems optimization.

Hongsheng Yang received his B.E. degree in automation, and an M.E. degree in control engineering from Fuzhou University, China, in 2019 and 2022, respectively. His research interests include tracking control and multi-agent systems.

Dongfang Li graduated from Nan**g University of Aeronautics and Astronautics in 2014, China. In 2021, he received his Ph.D. degree in mechanical engineering from the Bei**g University of Technology, China. He is currently an Associate Professor at the School of Electrical Engineering and Automation, Fuzhou University, Fuzhou, China. His research orientations are path-following control and obstacle avoidance control of snake robots.

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Huang, J., Chen, J., Yang, H. et al. Vehicle Platoon Tracking Control Based on Adaptive Neural Network Algorithm. Int. J. Control Autom. Syst. 21, 3405–3418 (2023). https://doi.org/10.1007/s12555-022-0445-5

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