基于演示切换跟踪的反馈控制律计算
Computation of Feedback Control Laws Based on Switched Tracking of Demonstrations
AI总结：
本文提出一种基于演示切换跟踪的反馈控制律综合算法，利用轨迹优化器提供的演示生成切换式反馈控制，相比模型预测控制实现更简单高效，且有严格收敛性与最优性保证，实验验证了其效率。
摘要：
机器人学中一种常见方法是通过对所谓演示者给出的特例进行泛化来学习任务。本文采用这一范式，提出一种算法，利用演示者（通常由轨迹优化器提供）自动综合反馈控制器，将由常微分方程描述的系统引导至目标集。所得反馈控制律在作为参考轨迹的各演示之间切换。与直接将轨迹优化用作控制律（例如模型预测控制形式）相比，该方法可实现更简单、更高效的控制器。该综合算法具备严格的收敛性和最优性结果，计算实验也证实了其效率。
英文摘要：
A common approach in robotics is to learn tasks by generalizing from special cases given by a so-called demonstrator. In this paper, we apply this paradigm and present an algorithm that uses a demonstrator (typically given by a trajectory optimizer) to automatically synthesize feedback controllers for steering a system described by ordinary differential equations into a goal set. The resulting feedback control law switches between the demonstrations that it uses as reference trajectories. In comparison to the direct use of trajectory optimization as a control law, for example, in the form of model predictive control, this allows for a much simpler and more efficient implementation of the controller. The synthesis algorithm comes with rigorous convergence and optimality results, and computational experiments confirm its efficiency.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
