论文标题
基于示范的切换跟踪的反馈控制法律计算
Computation of Feedback Control Laws Based on Switched Tracking of Demonstrations
论文作者
论文摘要
机器人技术中的一种常见方法是通过从所谓的演示者给出的特殊情况中概括来学习任务。在本文中，我们应用了此范式，并提出了一种算法，该算法使用演示器（通常由轨迹优化器给出）自动合成反馈控制器，以将普通微分方程描述的系统转向到目标集中。由此产生的反馈控制法在其用作参考轨迹的演示之间切换。与将轨迹优化作为控制定律的直接使用相比，例如，以模型预测控制的形式，这允许对控制器的更简单，更有效的实现。合成算法具有严格的合并和最佳结果，计算实验证实了其效率。
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.
