论文标题
强大的差分动态编程
Robust Differential Dynamic Programming
论文作者
论文摘要
差分动态编程是一种通常用于轨迹生成的最佳控制技术。该算法的许多变化已在文献中开发，包括随机动力学或状态和输入约束的算法。在这一贡献中，我们开发了一种强大的差异动态编程版本，该编程使用通用的植物和乘数放松来实现不确定性。为此，我们研究了Bellman原理的版本，并使用凸放松来解释动态程序中的不确定性。所得算法可以看作是非线性系统的强大轨迹生成工具。
Differential Dynamic Programming is an optimal control technique often used for trajectory generation. Many variations of this algorithm have been developed in the literature, including algorithms for stochastic dynamics or state and input constraints. In this contribution, we develop a robust version of Differential Dynamic Programming that uses generalized plants and multiplier relaxations for uncertainties. To this end, we study a version of the Bellman principle and use convex relaxations to account for uncertainties in the dynamic program. The resulting algorithm can be seen as a robust trajectory generation tool for nonlinear systems.
