控制理论(社会学)
弹道
跟踪(教育)
车辆动力学
跟踪误差
解耦(概率)
工程类
卡尔曼滤波器
理论(学习稳定性)
模型预测控制
计算机科学
模拟
控制工程
控制(管理)
人工智能
汽车工程
心理学
教育学
物理
天文
机器学习
作者
Yuhang Zhang,Weida Wang,Chao Yang,Tianqi Qie,Rong Sun
标识
DOI:10.1080/00423114.2024.2387044
摘要
Trajectory tracking is the core function of autonomous vehicle motion control. On variable road slopes, vehicles will encounter extra forces due to gravity, distinct from driving on flat roads. These forces will cause significant deviations in trajectory tracking and even instability, especially for four-wheel independent drive autonomous vehicles (4WIDAVs) with closely coupled longitudinal-lateral dynamics. To address above issues, first, a modified dynamics model considering coupled slopes is built, and the slope effect on vehicle motion and stability is analysed. Secondly, treating coupled slopes as augmented states, a square root cubature Kalman filter (SRCKF) is designed for real-time slope estimation. Then, a slope-adaptive model separate predictive trajectory tracking control strategy is proposed, decoupling the slope-adaptive tracking system into longitudinal and lateral subsystems. Predictive control is performed separately to ensure real-time performance, and prediction information is exchanged between subsystems to preserve dynamics coupled characteristics, enhancing tracking accuracy. Finally, the effectiveness of the proposed scheme is validated by simulations and real-vehicle experiment under the varying slope condition. Results show that the proposed scheme improves tracking accuracy by 17.59% and reduces computation time by 55.02% compared to existing scheme in simulations, and in experiment, the tracking error is reduced by 17.89%.
科研通智能强力驱动
Strongly Powered by AbleSci AI