发表机构
UC San Diego; King Abdullah University of Science and Technology (KAUST)（加州大学圣地亚哥分校; 阿卜杜拉国王科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对笛卡尔坐标系下车辆运动自行车模型的镇定局限，通过转换为极坐标并补充范围归一化坐标，开发光滑反馈律实现全局指数镇定，生成类人停车轨迹。
AI 中文摘要
在停车速度下，运动自行车模型是类车车辆的主流模型。尽管该模型应用广泛，但文献中针对该系统的镇定反馈律却很少，现有设计常无法复现真实的停车操作，这一局限源于笛卡尔坐标系，其中Brockett条件排除了光滑静态反馈镇定。我们通过将系统转换为极坐标并补充范围归一化坐标（编码类人停车操作的几何特征）来规避这一障碍。转换后的系统动力学呈现严格反馈形式，支持非常规反步设计，我们利用该特殊结构开发光滑反馈律，实现转换坐标系下的全局指数镇定，仅通过反馈即可生成类似人类驾驶员的停车轨迹。
英文摘要
At parking speeds, the kinematic bicycle is the prevailing model for car-like vehicles. Yet, despite its wide use, stabilizing feedback laws for this system are scarce in the literature, and existing designs often do not reproduce realistic parking maneuvers. This limitation is inherent to the Cartesian coordinates, where Brockett's condition rules out smooth static feedback stabilization. We bypass this obstruction by transforming the system into polar coordinates together with additional range-normalized coordinates that encode the geometry of human-like parking maneuvers. In the transformed coordinates, the dynamics take a strict-feedback form, enabling a nonconventional backstepping design. We exploit the particular structure to develop smooth feedback laws that achieve global exponential stabilization in the transformed coordinates which in turn generates parking trajectories resembling the one performed by human drivers through feedback alone.
