基于双层模糊逻辑方法的智能车辆行驶稳定性研究
Driving Stability of Intelligent Vehicles Based on Double-Layer Fuzzy Logic Method
DOI: 10.12677/ORF.2023.131039, PDF,   
作者: 郭婧博, 刘 飞：上海工程技术大学机械与汽车工程学院，上海
关键词: 模糊逻辑；自适应；分布式；稳定性；Fuzzy Logic； Adaptive； Distributed； Stability
摘要: 为提高智能车辆紧急工况下的自主维稳能力，提出一种基于双层模糊逻辑方法的行车稳定性自动调节策略。首先分析了车辆行驶稳定性影响参数，制定相应参数的理想约束范围，搭建车辆二自由度参考模型。其次将模型参数理想值与实际值偏差作为模糊逻辑输入量，然后引入额外比例因子和量化因子，实现内外双层自适应调节。最后将模糊逻辑输出的期望横摆力矩转换为纵向力矩，再基于摩擦椭圆理论分配给各轮，实现分布式前馈控制。在五次多项式路径下进行对比仿真试验，结果表明，所提出的双层模糊自适应调节策略可将车辆行驶稳定性提高20%以上。
Abstract: In order to improve the intelligent vehicles’ stability maintenance capability under emergency conditions, an automatic adjustment strategy of driving stability based on a two-layer fuzzy logic method is proposed. Firstly, the vehicle driving stability influencing parameters are analyzed, the ideal constraint range of corresponding parameters is formulated, and the vehicle two-degree-of-freedom reference model is built. Secondly, the deviation between the ideal and actual values of the model parameters is taken as the input quantity of the fuzzy logic, and then additional scaling factors and quantization factors are introduced to realize the internal and external two-layer adaptive adjustment. Finally, the desired yaw moment output from the fuzzy logic is converted into longitudinal moment, and then distribute to each wheel based on the friction ellipse theory to realize distributed feed forward control. Comparative simulation tests are conducted under five polynomial paths, and the results show that the proposed two-layer fuzzy adaptive regulation strategy can improve the vehicle driving stability by more than 20%.
文章引用：郭婧博, 刘飞. 基于双层模糊逻辑方法的智能车辆行驶稳定性研究[J]. 运筹与模糊学, 2023, 13(1): 371-381. https://doi.org/10.12677/ORF.2023.131039

参考文献

[1] 邵文博, 李骏, 张玉新, 王红. 智能汽车预期功能安全保障关键技术[J]. 汽车工程, 2022, 44(9): 1289-1304. [Google Scholar] [CrossRef] 
[2] He, X., Liu, Y., Lv, C., Ji, X. and Liu, Y. (2019) Emergency Steering Control of Autonomous Vehicle for Collision Avoidance and Stabilisation. Vehicle System Dynamics, 57, 1163-1187. [Google Scholar] [CrossRef] 
[3] Rachael, J., Rault, A., Testud, J.L. and Papon, J. (1978) Model Predictive Heuristic Control: Application to an Industrial Process. Automatica, 14, 413-428. [Google Scholar] [CrossRef] 
[4] Guo, N., Lenzo, B., Zhang, X., et al. (2020) A Real-Time Nonlinear Model Predictive Controller for Yaw Motion Optimization of Distributed Drive Electric Vehicles. IEEE Transactions on Vehicular Technology, 69, 4935-4946. [Google Scholar] [CrossRef] 
[5] Guo, N., Zhang, X., Zou, Y., Lenzo, B. and Zhang, T. (2020) A Computationally Efficient Path-Following Control Strategy of Autonomous Electric Vehicles with Yaw Motion Stabilization. IEEE Transactions on Transportation Electrification, 6, 728-739. [Google Scholar] [CrossRef] 
[6] Tian, Y., Yao, Q., Wang, C., et al. (2022) Switched Model Predictive Controller for Path Tracking of Autonomous Vehicle Considering Rollover Stability. Vehicle System Dynamics, 60, 4166-4185. [Google Scholar] [CrossRef] 
[7] 刘金琨, 孙富春. 滑模变结构控制理论及其算法研究与进展[J]. 控制理论与应用, 2007, 24(3): 407-418.
[8] Wang, K., Ding, W., Yang, M. and Zhu, Q. (2021) Dy-namic-Boundary-Based Lateral Motion Synergistic Control of Distributed Drive Autonomous Vehicle. Scientific Reports, 11, Article No. 22644. [Google Scholar] [CrossRef] [PubMed]
[9] Pamosoaji, A.K., Cat, P.T. and Hong, K.-S. (2014) Slid-ing-Mode and Proportional-Derivative-Type Motion Control with Radial Basis Function Neural Network Based Es-timators for Wheeled Vehicles. International Journal of Systems Science, 45, 2515-2528. [Google Scholar] [CrossRef] 
[10] Taghavifar, H., Hu, C., Taghavifar, L., et al. (2020) Op-timal Robust Control of Vehicle Lateral Stability Using Damped Least-Square Backpropagation Training of Neural Networks. Neurocomputing, 384, 256-267. [Google Scholar] [CrossRef] 
[11] Gutierrez, J., Romo, J., Gonz´lez, M.I., Canibano, E. and Merino, J.C. (2011) Control Algorithm Development for Independent Wheel Torque Distribution with 4 In-wheel Electric Motors. 2011 UKSim 5th European Symposium on Computer Modeling and Simulation, Madrid, 16-18 November 2011, 257-262. [Google Scholar] [CrossRef] 
[12] Fors, V., Olofsson, B. and Nielsen, L. (2020) Attainable Force Volumes of Optimal Autonomous at-the-Limit Vehicle Manoeuvres. Vehicle System Dy-namics, 58, 1101-1122. [Google Scholar] [CrossRef] 
[13] Zadeh, L.A. (1965) Fuzzy Sets. Information and Control, 8, 338-353. [Google Scholar] [CrossRef] 
[14] Peng, C. and Chen, L. (2022) Model Reference Adaptive Control Based on Adjustable Reference Model during Mode Transition for Hybrid Electric Vehicles. Mechatronics, 87, Article ID: 102894. [Google Scholar] [CrossRef] 
[15] Guo, J., Li, L., Li, K. and Wang, R. (2013) An Adaptive Fuzzy-Sliding Lateral Control Strategy of Automated Vehicles Based on Vision Navigation. Vehicle System Dynamics, 51, 1502-1517. [Google Scholar] [CrossRef] 
[16] Zhang, P., Chen, W., Wang, H., et al. (2022) Fuzzy PID Control Method for Damping of Electronically Controlled Air Suspension Shock Absorbers for Vehicles. Engi-neering Research Express, 4, Article ID: 045020. [Google Scholar] [CrossRef] 
[17] Rajamani, E. (2012) Vehicle Dynamics and Control. Springer, New York.
[18] Ryu, J., Lee, J.S. and Kim, H. Evaluation of a Direct Yaw Moment Control Algorithm by Brake Hardware-in-the-Loop Simulation. Proceedings of AVEC98 Symposium, Nagoya, 14-18 September 1998, 231-236.
[19] 郭孔辉. 各向摩擦系数不同条件下轮胎力学特性的统一理论模型[J]. 中国机械工程, 1996(4): 90-93+125-126.

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