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Cascade Optimization Control of Unmanned Vehicle Path Tracking Under Harsh Driving Conditions

恶劣行驶条件下无人车辆路径跟踪串级优化控制

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Abstract

Under ultra-high-speed and harsh conditions, conventional control methods struggle to ensure the path tracking accuracy and driving stability of unmanned vehicles during the turning process. Therefore, this study proposes a cascade control to solve this problem. Based on the new vehicle error model that considers vehicle tire sideslip and road curvature, the feedforward-parametric adaptive linear quadratic regulator (LQR) and proportional integral control-based speed-keeping controllers are used to compose the path-tracking cascade optimization controller for unmanned vehicles. To improve the adaptability of the unmanned vehicle path-tracking control under harsh driving conditions, the LQR controller parameters are automatically adjusted using a back-propagation neural network, in which the initial weights and thresholds are optimized using the improved grey wolf optimization algorithm according to the driving conditions. The speed-keeping controller reduces the impact on the curve-tracking accuracy under nonlinear vehicle speed variations. Finally, a joint model of MATLAB/Simulink and CarSim was established, and simulations show that the proposed control method can achieve stable entry and exit curves at ultra-high speeds for unmanned vehicles. Under strong wind and ice road conditions, the method exhibits a higher tracking accuracy and is more adaptive and robust to external interference in driving and variable curvature roads than methods such as the feedforward-LQR, preview and pure pursuit controls.

摘要

在超高速以及恶劣行驶条件下, 传统控制方法难以同时保证无人车在转弯过程中的路径跟踪精度和驾驶稳定性。因此, 本研究提出一种无人车辆路径跟踪串级控制策略。考虑基于车辆轮胎侧滑和道路曲率影响建立一种新的车辆误差模型, 采用前馈-参数自适应线性二次调节器 （LQR） 和比例积分 （PI） 控制算法设计速度保持控制器来组成无人车路径跟踪串级控制器。为提高无人车在恶劣驾驶条件下的适应性, 使用BP神经网络自动调整LQR控制器参数, 其中初始权值和阈值根据驾驶条件使用改进的灰狼优化算法进行优化; 保速控制器提高了在非线性车速变化下的曲线跟踪精度。最后, 建立MATLAB/Simulink和CarSim的联合模型, 仿真结果表明: 所提出的控制方法可以实现无人车在超高速下的保速过弯能力。在强风和冰雪路面条件下, 该方法表现出更高的跟踪精度, 与前馈-LQR、单点预瞄控制和纯追踪控制等方法相比, 在驾驶和变曲率路面上对外界干扰的适应性和鲁棒性更强。

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References

  1. World Health Organization. Global status report on road safety 2013: Supporting a decade of action [R]. Geneva: WHO, 2013.

    Google Scholar 

  2. GIETELINK O, PLOEG J, DE SCHUTTER B, et al. Development of advanced driver assistance systems with vehicle hardware-in-the-loop simulations [J]. Vehicle System Dynamics, 2006, 44(7): 569–590.

    Article  Google Scholar 

  3. BAI G X, MENG Y, LIU L, et al. Current status of path tracking control of unmanned driving vehicles [J]. Chinese Journal of Engineering, 2021, 43(4): 475–485 (in Chinese).

    Google Scholar 

  4. YIN Q H, LI J, LIANG Z P, et al. Path tracking control system of robot based on fuzzy neural network-PID [J]. Journal of Chinese Agricultural Mechanization, 2020, 41(5): 182–187 (in Chinese).

    Google Scholar 

  5. HAN G N, FU W P, WANG W, et al. The lateral tracking control for the intelligent vehicle based on adaptive PID neural network [J]. Sensors, 2017, 17(6): 1244.

    Article  Google Scholar 

  6. BU X H, HOU Z S, CHI R H. Model free adaptive iterative learning control for farm vehicle path tracking [J]. IFAC Proceedings Volumes, 2013, 46(20): 153–158.

    Article  Google Scholar 

  7. XIA Y Q, PU F, LI S F, et al. Lateral path tracking control of autonomous land vehicle based on ADRC and differential flatness [J]. IEEE Transactions on Industrial Electronics, 2016, 63(5): 3091–3099.

    Article  Google Scholar 

  8. HUANG H Y, ZHANG J, WANG Y, et al. Path tracking for intelligent vehicle based on the optimal multipoint preview control [J]. Automobile Technology, 2018(10): 6–9 (in Chinese).

    Google Scholar 

  9. CHEN Y, CHEN S Z, REN H B, et al. Path tracking and handling stability control strategy with collision avoidance for the autonomous vehicle under extreme conditions [J]. IEEE Transactions on Vehicular Technology, 2020, 69(12): 14602–14617.

    Article  Google Scholar 

  10. SHI Q, ZHANG J L, YANG M. Curvature adaptive control based path following for automatic driving vehicles in private area [J]. Journal of Shanghai Jiao Tong University (Science), 2021, 26(5): 690–698.

    Google Scholar 

  11. QU T, ZHAO J W, GAO H H, et al. Multi-mode switching-based model predictive control approach for longitudinal autonomous driving with acceleration estimation [J]. IET Intelligent Transport Systems, 2020, 14(14): 2102–2112.

    Article  Google Scholar 

  12. JIANG L B, HONG S. Research on the application of intelligent vehicle path tracking control [J]. Machine Design and Manufacturing Engineering, 2021, 50(6): 51–55 (in Chinese).

    MathSciNet  Google Scholar 

  13. KOU F R, YANG H J, ZHANG X Q, et al. A lateral control strategy for unmanned vehicle path tracking using state feedback [J]. Mechanical Science and Technology for Aerospace Engineering, 2022, 41(1): 143–150 (in Chinese).

    Google Scholar 

  14. WU X T, WEI C, ZHAI J K, et al. Study on the optimization of autonomous vehicle on path-following considering yaw stability [J]. Journal of Mechanical Engineering, 2022, 58(6): 130–142 (in Chinese).

    Google Scholar 

  15. XU F, ZHANG J M, HU Y F, et al. Lateral and longitudinal coupling real-time predictive controller for intelligent vehicle path tracking [J]. Journal of Jilin University (Engineering and Technology Edition), 2021, 51(6): 2287–2294 (in Chinese).

    Google Scholar 

  16. ZHOU D S, LI W, LIU Y L, et al. Research on linear quadratic path tracking based on receding horizon [J]. Automobile Technology, 2017(10): 54–57 (in Chinese).

    Google Scholar 

  17. NI L Q, LIN F, WANG K Z. Research on path-following control of intelligent vehicles based on preview model [J]. Journal of Chongqing University of Technology (Natural Science), 2017, 31(3): 27–33 (in Chinese).

    Google Scholar 

  18. GAO L L, TANG F M, GUO P, et al. Research on improved LQR control for self-driving vehicle lateral motion [J]. Mechanical Science and Technology for Aerospace Engineering, 2021, 40(3): 435–441 (in Chinese).

    Google Scholar 

  19. MENG Y, WANG Y, GU Q, et al. LQR-GA path tracking control of articulated vehicle based on predictive information [J]. Transactions of the Chinese Society for Agricultural Machinery, 2018, 49(6): 375–384 (in Chinese).

    Google Scholar 

  20. YANG Z J, MAO L, YAN B, et al. Performance analysis and prediction of asymmetric two-level priority polling system based on BP neural network [J]. Applied Soft Computing, 2021, 99: 106880.

    Article  Google Scholar 

  21. ZHANG J P, GAO P F, FANG F. An ATPSO-BP neural network modeling and its application in mechanical property prediction [J]. Computational Materials Science, 2019, 163: 262–266.

    Article  Google Scholar 

  22. WANG H Y, LI J Z, LIU L M. Process optimization and weld forming control based on GA-BP algorithm for riveting-welding hybrid bonding between magnesium and CFRP [J]. Journal of Manufacturing Processes, 2021, 70: 97–107.

    Article  Google Scholar 

  23. CHEN L, YANG X T, SUN C H, et al. Feed intake prediction model for group fish using the MEA-BP neural network in intensive aquaculture [J]. Information Processing in Agriculture, 2020, 7(2): 261–271.

    Article  Google Scholar 

  24. MIRJALILI S, MIRJALILI S M, LEWIS A. Grey wolf optimizer [J]. Advances in Engineering Software, 2014, 69: 46–61.

    Article  Google Scholar 

  25. WANG T, REN S, SI F, et al. Online optimization of a WFGD system based on GWO-BP neural network algorithm [J]. Power Equipment, 2021, 35(2): 122–130.

    Google Scholar 

  26. TRIPATHI A K, SHARMA K, BALA M J. A novel clustering method using enhanced grey wolf optimizer and MapReduce [J]. Big Data Research, 2018, 14: 93–100.

    Article  Google Scholar 

  27. MITTAL N, SINGH U, SOHI B S. Modified grey wolf optimizer for global engineering optimization [J]. Applied Computational Intelligence and Soft Computing, 2016, 2016: 7950348.

    Article  Google Scholar 

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Funding

Foundation item: the Natural Science Foundation of Guangxi (No. 2020GXNSFDA238011), the Open Fund Project of Guangxi Key Laboratory of Automation Detection Technology and Instrument (No. YQ21203), and the Independent Research Project of Guangxi Key Laboratory of Auto Parts and Vehicle Technology (No. 2020GKLACVTZZ02)

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Authors and Affiliations

  1. School of Electrical and Information Engineering; Guangxi Key Laboratory of Auto Parts and Vehicle Technology, Guangxi University of Science and Technology, Liuzhou, Guangxi, 545006, China

    Yinggang Huang  (黄迎港), Wenguang Luo  (罗文广) & Hongli Lan  (蓝红莉)

  2. School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, 510641, China

    Dan Huang  (黄丹)

Authors
  1. Yinggang Huang  (黄迎港)
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  2. Wenguang Luo  (罗文广)
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  3. Dan Huang  (黄丹)
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  4. Hongli Lan  (蓝红莉)
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Corresponding author

Correspondence to Wenguang Luo  (罗文广).

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Huang, Y., Luo, W., Huang, D. et al. Cascade Optimization Control of Unmanned Vehicle Path Tracking Under Harsh Driving Conditions. J. Shanghai Jiaotong Univ. (Sci.) 28, 114–125 (2023). https://doi.org/10.1007/s12204-023-2574-2

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  • DOI: https://doi.org/10.1007/s12204-023-2574-2

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