Chinese Journal of Mechanical Engineering >

2021 , Vol. 34 >Issue 4: 74 - 74

DOI: https://doi.org/10.1186/s10033-021-00590-3

Advanced Transportation Equipment

Comparative Study of Trajectory Tracking Control for Automated Vehicles via Model Predictive Control and Robust H-infinity State Feedback Control

  • Kai Yang ,
  • Xiaolin Tang ,
  • Yechen Qin ,
  • Yanjun Huang ,
  • Hong Wang ,
  • Huayan Pu
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  • 1. College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing, 400000, China;
    2. School of Mechanical Engineering, Beijing Institute of Technology, Beijing, 100080, China;
    3. School of Automotive Studies, Tongji University, Shanghai, 201804, China;
    4. School of Vehicle and Mobility, Tsinghua University, Beijing, 100080, China

收稿日期: 2020-05-14

  修回日期: 2021-06-03

  网络出版日期: 2021-12-21

基金资助

Supported by Natural Science Foundation of China (Grant Nos. 52072051, 51705044), Chongqing Municipal Natural Science Foundation of China (Grant No. cstc2020jcyj-msxmX0956), State Key Laboratory of Mechanical System and Vibration (Grant No. MSV202016), and State Key Laboratory of Mechanical Transmissions (Grant No. SKLMT-KFKT-201806).

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Comparative Study of Trajectory Tracking Control for Automated Vehicles via Model Predictive Control and Robust H-infinity State Feedback Control

  • Kai Yang ,
  • Xiaolin Tang ,
  • Yechen Qin ,
  • Yanjun Huang ,
  • Hong Wang ,
  • Huayan Pu
Expand
  • 1. College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing, 400000, China;
    2. School of Mechanical Engineering, Beijing Institute of Technology, Beijing, 100080, China;
    3. School of Automotive Studies, Tongji University, Shanghai, 201804, China;
    4. School of Vehicle and Mobility, Tsinghua University, Beijing, 100080, China

Received date: 2020-05-14

  Revised date: 2021-06-03

  Online published: 2021-12-21

Supported by

Supported by Natural Science Foundation of China (Grant Nos. 52072051, 51705044), Chongqing Municipal Natural Science Foundation of China (Grant No. cstc2020jcyj-msxmX0956), State Key Laboratory of Mechanical System and Vibration (Grant No. MSV202016), and State Key Laboratory of Mechanical Transmissions (Grant No. SKLMT-KFKT-201806).

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摘要

A comparative study of model predictive control (MPC) schemes and robust \begin{document}$H_{\infty }$\end{document} state feedback control (RSC) method for trajectory tracking is proposed in this paper. The main objective of this paper is to compare MPC and RSC controllers' performance in tracking predefined trajectory under different scenarios. MPC controller is designed based on the simple longitudinal-yaw-lateral motions of a single-track vehicle with a linear tire, which is an approximation of the more realistic model of a vehicle with double-track motion with a non-linear tire mode. RSC is designed on the basis of the same method as adopted for the MPC controller to achieve a fair comparison. Then, three test cases are built in CarSim-Simulink joint platform. Specifically, the verification test is used to test the tracking accuracy of MPC and RSC controller under well road conditions. Besides, the double lane change test with low road adhesion is designed to find the maximum velocity that both controllers can carry out while guaranteeing stability. Furthermore, an extreme curve test is built where the road adhesion changes suddenly, in order to test the performance of both controllers under extreme conditions. Finally, the advantages and disadvantages of MPC and RSC under different scenarios are also discussed.

关键词： Trajectory tracking; Automated vehicles; Model predictive control; Robust H∞ state feedback contro

本文引用格式

Kai Yang , Xiaolin Tang , Yechen Qin , Yanjun Huang , Hong Wang , Huayan Pu . Comparative Study of Trajectory Tracking Control for Automated Vehicles via Model Predictive Control and Robust H-infinity State Feedback Control[J]. Chinese Journal of Mechanical Engineering, 2021 , 34(4) : 74 -74 . DOI: 10.1186/s10033-021-00590-3

Abstract

A comparative study of model predictive control (MPC) schemes and robust \begin{document}$H_{\infty }$\end{document} state feedback control (RSC) method for trajectory tracking is proposed in this paper. The main objective of this paper is to compare MPC and RSC controllers' performance in tracking predefined trajectory under different scenarios. MPC controller is designed based on the simple longitudinal-yaw-lateral motions of a single-track vehicle with a linear tire, which is an approximation of the more realistic model of a vehicle with double-track motion with a non-linear tire mode. RSC is designed on the basis of the same method as adopted for the MPC controller to achieve a fair comparison. Then, three test cases are built in CarSim-Simulink joint platform. Specifically, the verification test is used to test the tracking accuracy of MPC and RSC controller under well road conditions. Besides, the double lane change test with low road adhesion is designed to find the maximum velocity that both controllers can carry out while guaranteeing stability. Furthermore, an extreme curve test is built where the road adhesion changes suddenly, in order to test the performance of both controllers under extreme conditions. Finally, the advantages and disadvantages of MPC and RSC under different scenarios are also discussed.

Key words： Trajectory tracking; Automated vehicles; Model predictive control; Robust H∞ state feedback contro

参考文献

[1] D González, J Pérez, V Milanés, et al. A review of motion planning techniques for automated vehicles. IEEE Transactions on Intelligent Transportation Systems, 2015, 17(4): 1135-1145.
[2] Y Qin, X Tang, T Jia, et al. Noise and vibration suppression in hybrid electric vehicles: state of the art and challenges. Renewable and Sustainable Energy Reviews, 2020, 124: 109782.
[3] X Tang, S Li, H Wang, et al. Research on energy control strategy based on hierarchical model predictive control in connected environment. Journal of Mechanical Engineering, 2020, 56(14): 119-128. (in Chinese)
[4] T Liu, X Tang, H Wang, et al. Adaptive hierarchical energy management design for a plug-in hybrid electric vehicle. IEEE Transactions on Vehicular Technology, 2019, 68(12): 11513-11522.
[5] C Katrakazas, M Quddus, W H Chen, et al. Real-time motion planning methods for autonomous on-road driving: State-of-the-art and future research directions. Transportation Research Part C: Emerging Technologies, 2015, 60: 416-442.
[6] X Yang, X Tang, K Yang, et al. Local motion planning framework for autonomous vehicle considering position uncertainty in highway. 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI), Hangzhou, China, December, 2020: 471-474.
[7] R Yang, R Xiong, F C Sun. Experimental platform development and characteristics analysis of external short circuit in lithium-ion batteries. Journal of Electrical Engineering. 2021, 16(1): 103-118. (in Chinese)
[8] B Paden, M ?áp, S Yong, et al. A survey of motion planning and control techniques for self-driving urban vehicles. IEEE Transactions on Intelligent Vehicles, 2016, 1(1): 33-35.
[9] Y Huang, H Wang, A Khajepour, et al. A novel local motion planning framework for autonomous vehicles based on resistance network and model predictive control. IEEE Transactions on Vehicular Technology, 2019, 69(1): 55-66.
[10] C Hu, H Jing, R R Wang, et al. Robust H∞ output-feedback control for path following of autonomous ground vehicles. Mechanical Systems and Signal Processing, 2016, 70: 414-427.
[11] M Samuel, M Hussein, M B Mohamad. A review of some pure-pursuit based path tracking techniques for control of autonomous vehicle. International Journal of Computer Applications, 2016, 135(1): 35-38.
[12] S Xu, H Peng. Design, analysis, and experiments of preview path tracking control for autonomous vehicles. IEEE Transactions on Intelligent Transportation Systems, 2019, 21(1): 48-58.
[13] Y Huang, H Ding, Y Zhang, et al. A motion planning and tracking framework for autonomous vehicles based on artificial potential field elaborated resistance network approach. IEEE Transactions on Industrial Electronics, 2019, 67(2): 1376-1386.
[14] Y Zhang, X Wang, H Yang, et al. Robust predictive current control of induction motors based on linear extended state observer. Chinese Journal of Electrical Engineering, 2021, 7(1): 94-105.
[15] R Rajamani. Vehicle dynamics and control. Springer Science & Business Media, 2011.
[16] R Attia, R Orjuela, M Basset. Combined longitudinal and lateral control for automated vehicle guidance. Vehicle System Dynamics, 2014, 52(2): 261-279.
[17] F Borrelli, P Falcone, T Keviczky, et al. MPC-based approach to active steering for autonomous vehicle systems. International Journal of Vehicle Autonomous Systems, 2005, 3(2): 265-291.
[18] P Falcone, F Borrelli, J Asgari, et al. Predictive active steering control for autonomous vehicle systems. IEEE Transactions on Control Systems Technology, 2007, 15(3): 566-580.
[19] J Ji, A Khajepour, W Melek, et al. Path planning and tracking for vehicle collision avoidance based on model predictive control with multi-constraints. IEEE Transactions on Vehicular Technology, 2016, 66(2): 952-964.
[20] L Chen, H Nian, Y X. Improved model predictive direct power control of grid side converter in weak grid using Kalman filter and DSOGI. Chinese Journal of Electrical Engineering, 2019, 5(4): 22-32.
[21] J Guo, Y Luo, K Li. Robust gain-scheduling automatic steering control of unmanned ground vehicles under velocity-varying motion. Vehicle System Dynamics, 2019, 57(4): 595-616.
[22] L Shen, H Xiao. Delay-dependent robust stability analysis of power systems with PID controller. Chinese Journal of Electrical Engineering, 2019, 5(2): 79-86.
[23] D Q Mayne. Model predictive control: Recent developments and future promise. Automatica, 2014, 50(12): 2967-2986.
[24] F Yakub, Y Mori. Comparative study of autonomous path-following vehicle control via model predictive control and linear quadratic control. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 2015, 229(12): 1695-1714.
[25] D Q Mayne, J B Rawlings, C V Rao, et al. Constrained model predictive control: Stability and optimality. Automatica, 2000, 36(6): 789-814.
[26] X L Tang, T Jia, X S Hu, et al. Naturalistic data-driven predictive energy management for plug-in hybrid electric vehicles. IEEE Transactions on Transportation Electrification, 2021, 7(2): 497-508.
[27] N Wada, H Tomosugi, M Saeki. Model predictive tracking control for a linear system under time‐varying input constraints. International Journal of Robust and Nonlinear Control, 2013, 23(9): 945-964.
[28] C Hu, L Zhao, L Cao, et al. Steering control based on model predictive control for obstacle avoidance of unmanned ground vehicle. Measurement and Control, 2020, 53(3-4): 501-518.
[29] R Wang, H Jing, C Hu, et al. Robust H∞ path following control for autonomous ground vehicles with delay and data dropout. IEEE Transactions on Intelligent Transportation Systems, 2016, 17(7): 2042-2050.
[30] H Zhang, X Zhang, J Wang. Robust gain-scheduling energy-to-peak control of vehicle lateral dynamics stabilisation. Vehicle System Dynamics, 2014, 52(3): 309-340.
[31] H Du, N Zhang, F Naghdy. Velocity-dependent robust control for improving vehicle lateral dynamics. Transportation Research Part C: Emerging Technologies, 2011, 19(3): 454-468.
[32] X Huang, H Zhang, G Zhang, et al. Robust weighted gain-scheduling H∞ vehicle lateral motion control with considerations of steering system backlash-type hysteresis. IEEE Transactions on Control Systems Technology, 2014, 22(5): 1740-1753.
[33] Y Ren. Research on active tracking and collision avoidance control of intelligent electric vehicles. Chongqing: Chongqing University, 2018. (in Chinese)
[34] S Boyd, E L Ghaoui, E Feron, et al. Linear matrix inequalities in system and control theory. Society for Industrial and Applied Mathematics, Philadelphia, USA, 1994.
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