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A Comparison Study of Kinematic and Dynamic Models for Trajectory Tracking of Autonomous Vehicles Using Model Predictive Control

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Abstract

Efficient trajectory tracking approaches can enable autonomous vehicles not only to get a smooth trajectory but to achieve a lower energy dissipation. Since vehicle model plays an important role in trajectory tracking, this paper investigates and compares the performance of two classical vehicle models for trajectory tracking of autonomous vehicles using model predictive control (MPC). Firstly, a two-degree-of-freedom kinematic model and a three-degree-of-freedom yaw dynamic model are established for autonomous vehicles. Meanwhile, in order to carry out tracking control more effectively and smoothly, the tire slip angle has been taken into account by the dynamic model. Then, we design two MPC controllers for trajectory tracking, which are based on the kinematic model and the dynamic model, respectively. The performances of two MPC controllers are evaluated and compared on the Carsim/Matlab joint simulation platform. Experimental results demonstrated that, under low speed working conditions, both two MPC controllers can follow the reference trajectory with high accuracy and stability. However, under high speed working conditions, the tracking error of the kinematic model is too large to be used in the real trajectory tracking problem. On the contrary, the controller based on the dynamic model still performs a good tracking effect. In addition, this study offers a guidance on how to select a suitable vehicle model for autonomous vehicles under different speed working conditions.

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Authors and Affiliations

  1. School of Information Science and Engineering, Jiaxing University, Jiaxing, Zhejiang, 314001, China

    Bao-Lin Ye & Shaofeng Niu

  2. Zhejiang Sci-tech University, Hangzhou, Zhejiang, 310018, China

    Bao-Lin Ye

  3. School of Computer Science and Technology, Zhejiang Sci-tech University, **asha Campus, Hangzhou, Zhejiang, 310018, China

    Shaofeng Niu

  4. Department of Electrical and Computer Engineering, Purdue School of Engineering and Technology, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202-5132, USA

    Lingxi Li

  5. State Key Laboratory of Industrial Control Technology, Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou, 310027, China

    Weimin Wu

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  1. Bao-Lin Ye
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Correspondence to Bao-Lin Ye.

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The authors declare that there is no competing financial interest or personal relationship that could have appeared to influence the work reported in this paper. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

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This work was supported in part by Zhejiang Provincial Natural Science Foundation of China under Grant No.LTGS23F030002; and by the Science and Technology Program of Zhejiang Province of China under Grant No.2023C01174; and by the Jiaxing Public Welfare Research Program No.2023AY11034; and by the Open Research Projectof the State Key Laboratory of Industrial Control Technology, Zhejiang University, China No.ICT2022B52; and by the National Natural Science Foundation of China under Grant No.61603154.

Bao-Lin Ye is currently an associate professor in the Department of Electronic and Information Engineering at the School of Information Science and Engineering, Jiaxing University, Jiaxing, Zhejiang, China. He received his Ph.D. degree from Zhejiang University, Hangzhou, China, in June 2015. He was a Visiting Scholar at Indiana University-Purdue University Indianapolis, Indianapolis, IN, USA, from 2018 to 2019. His research interests include deep learning, reinforcement learning, intelligent control theory, and autonomous vehicle control.

Shaofeng Niu is pursuing an M.S. degree in computer technology at Zhejiang Sci-tech University, **asha Campus, Hangzhou, Zhejiang, China. He received his B.E. degree from Ningbo University of Technology, Ningbo, Zhejiang, China in 2020. His research interests include model predictive control, autonomous vehicles, and artificial intelligence.

Lingxi Li is currently a professor in the Department of Electrical and Computer Engineering at Purdue School of Engineering and Technology, Indiana University-Purdue University Indianapolis, USA. He received his Ph.D. degree in electrical and computer engineering from the University of Illinois at Urbana-Champaign in 2008. He has authored/co-authored one book and over 130 research articles in refereed journals and conferences. He is currently serving as an associate editor for five international journals and has served as general chair, program chair, program co-chair, publication chair, etc., for more than 30 international conferences. His current research focuses on modeling, analysis, control, and optimization of complex systems, connected and automated vehicles, intelligent transportation systems, intelligent vehicles, discrete event dynamic systems, and human machine interactions.

Weimin Wu received his B.S. degree in electrical engineering, an M.S. degree in computer engineering from the Taiyuan University of Science and Technology, Taiyuan, China, and a Ph.D. degree in control science and engineering from Zhejiang University, Hangzhou, China, in 1996, 1999, and 2002, respectively. In 2003, he joined Zhejiang University, where he is currently a Professor with the College of Control Science and Engineering. He was a Visiting Scholar with the Georgia Institute of Technology, Atlanta, GA, USA, from 2007 to 2008, and with the New Jersey Institute of Technology, Newark, NJ, USA, from 2008 to 2009. His research interests include discrete event systems and its applications in manufacturing, transportation, self-driving, and logistics automation systems. He served as an Associate Editor for the IEEE Transactions on Automation Science and Engineering and Asian Journal of Control.

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Ye, BL., Niu, S., Li, L. et al. A Comparison Study of Kinematic and Dynamic Models for Trajectory Tracking of Autonomous Vehicles Using Model Predictive Control. Int. J. Control Autom. Syst. 21, 3006–3021 (2023). https://doi.org/10.1007/s12555-022-0337-8

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