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首页> 外文期刊>Intelligent Transport Systems, IET >Integrated model predictive and torque vectoring control for path tracking of 4-wheel-driven autonomous vehicles
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Integrated model predictive and torque vectoring control for path tracking of 4-wheel-driven autonomous vehicles

机译:集成模型预测和转矩矢量控制,用于四轮驱动自动驾驶汽车的路径跟踪

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摘要

In this study, an integrated path tracking control framework is proposed for the independent-driven autonomous electric vehicles. The proposed control scheme includes three parts: the non-linear model predictive path tracking controller, the lateral stability controller, and the optimal torque vectoring controller. Firstly, the upper bound speed limit is regulated based on the known curvature and adhesion coefficient of the road to prevent the tyre saturation. The model predictive controller generates the steering angle and the desired longitudinal force for path tracking. Simultaneously, the lateral stability controller calculates the desired yaw moment to balance the vehicle stability and motility under different situations. Finally, the optimal torque vectoring controller distributes the wheel torques to generate the desired longitudinal force and yaw moment. Three test cases are designed and verified based on a Carsim/Simulink platform to evaluate the control performance. The test results illustrate that the proposed control framework has satisfactory path tracking performance, and the desired balance between vehicle mobility and stability is achieved under different road conditions.
机译:在这项研究中,提出了一种用于独立驾驶的自主电动汽车的集成路径跟踪控制框架。所提出的控制方案包括三个部分:非线性模型预测路径跟踪控制器,横向稳定性控制器和最佳转矩矢量控制器。首先,基于已知的道路曲率和附着系数来调节上限速度限制,以防止轮胎饱和。模型预测控制器生成转向角和所需的纵向力以进行路径跟踪。同时,横向稳定性控制器计算所需的横摆力矩,以平衡不同情况下的车辆稳定性和动力。最后,最佳扭矩矢量控制器分配车轮扭矩以生成所需的纵向力和横摆力矩。基于Carsim / Simulink平台设计并验证了三个测试用例,以评估控制性能。测试结果表明,所提出的控制框架具有令人满意的路径跟踪性能,并在不同的道路条件下实现了车辆机动性与稳定性之间的理想平衡。

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