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Lateral Model Predictive Control for Over-Actuated Autonomous Vehicle

机译:过度驾驶自动驾驶汽车的横向模型预测控制

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In this paper, a lateral controller is proposed for an over-actuated vehicle. The controller is formulated as a linear time-varying model predictive controller. The aim of the controller is to track a desired path smoothly, by making use of the vehicle crabbing capability (sideways movement) and minimizing the magnitude of curvature used. To do this, not only the error to the path is minimized, but also the error to the desired orientation and the control signals requests. The controller uses an extended kinematic model that takes into consideration the vehicle crabbing capability and is able to track not only kinematically feasible paths, but also plan and track over non-feasible discontinuous paths. Ackermann steering geometry is used to transform the control requests, curvature, and crabbing angle, to wheel angles. Finally, the controller performance is evaluated first by simulation and, after, by means of experimental tests on an over-actuated autonomous research vehicle.
机译:在本文中,提出了一种用于过度驱动车辆的横向控制器。该控制器被公式化为线性时变模型预测控制器。控制器的目的是通过利用车辆抓取能力(侧向移动)并最小化所使用的曲率幅度来平滑地跟踪所需的路径。为此,不仅将路径误差减小到最小,而且将期望方向的误差减小到控制信号的要求。控制器使用扩展的运动学模型,该模型考虑了车辆抓取能力,不仅能够跟踪运动学上可行的路径,而且还能计划和跟踪不可行的不连续路径。 Ackermann转向几何用于将控制请求,曲率和抓角转换为车轮角度。最后,首先通过仿真评估控制器的性能,然后通过在过度驱动的自主研究工具上进行的实验测试来评估控制器的性能。

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