论文标题
非全面工具的模型预测控制:超出差速器驱动器
Model Predictive Control of Non-Holonomic Vehicles: Beyond Differential-Drive
论文作者
论文摘要
非全面工具具有巨大的实用价值,并且越来越多地自动化。但是,可以准确地控制它们,例如,当停车场对自动控制方法(包括模型预测控制(MPC))的挑战。本文结合了MPC理论和亚帝国的几何形状的结果,本文提出了一个全面的,即现成的设计程序,用于将MPC控制器转移到给定的设定点中。可以确定的是,所得控制器名义上渐近地稳定了大型预测范围的设定值。设计程序被示例应用于四辆车,包括运动型汽车和一个差异驱动的移动机器人,最多可拖车。控制器使用针对非全面运动学的非二次成本函数。在新颖的情况下,对于被考虑的示例车辆,事实证明,在其他类似的控制器中使用的二次成本不足以可靠地渐近地稳定闭环。由于二次成本是控制的常规选择,因此这突出了调查结果的相关性。据作者的了解,这是该提议结构的MPC控制器首次应用于非独立车辆,尤其是(部分)(部分)在硬件上。
Non-holonomic vehicles are of immense practical value and increasingly subject to automation. However, controlling them accurately, e.g., when parking, is known to be challenging for automatic control methods, including model predictive control (MPC). Combining results from MPC theory and sub-Riemannian geometry in the form of homogeneous nilpotent system approximations, this paper proposes a comprehensive, ready-to-apply design procedure for MPC controllers to steer controllable, driftless non-holonomic vehicles into given setpoints. It can be ascertained that the resulting controllers nominally asymptotically stabilize the setpoint for a large-enough prediction horizon. The design procedure is exemplarily applied to four vehicles, including the kinematic car and a differentially driven mobile robot with up to two trailers. The controllers use a non-quadratic cost function tailored to the non-holonomic kinematics. Novelly, for the considered example vehicles, it is proven that a quadratic cost employed in an otherwise similar controller is insufficient to reliably asymptotically stabilize the closed loop. Since quadratic costs are the conventional choice in control, this highlights the relevance of the findings. To the knowledge of the authors, it is the first time that MPC controllers of the proposed structure are applied to non-holonomic vehicles beyond very simple ones, in particular (partly) on hardware.