论文标题
非线性随机最佳控制的顺序凸编程
Sequential Convex Programming For Non-Linear Stochastic Optimal Control
论文作者
论文摘要
这项工作引入了一个连续的凸编程框架，用于非线性，有限维的随机最佳控制，其中不确定性是由多维Wiener过程建模的。我们证明，在随机pontryagin最大原理的意义上，通过顺序凸面编程生成的迭代序列的任何积累点都是原始问题的本地候选解决方案。此外，我们为至少一个这样的积累点提供了足够的条件。然后，我们利用这些属性来设计一种实用的数值方法，用于基于随机顺序凸编程的确定性转录来解决非线性随机最佳控制问题。
This work introduces a sequential convex programming framework for non-linear, finite-dimensional stochastic optimal control, where uncertainties are modeled by a multidimensional Wiener process. We prove that any accumulation point of the sequence of iterates generated by sequential convex programming is a candidate locally-optimal solution for the original problem in the sense of the stochastic Pontryagin Maximum Principle. Moreover, we provide sufficient conditions for the existence of at least one such accumulation point. We then leverage these properties to design a practical numerical method for solving non-linear stochastic optimal control problems based on a deterministic transcription of stochastic sequential convex programming.
