Sequential Convex Programming For Non-Linear Stochastic Optimal Control
Sequential Convex Programming For Non-Linear Stochastic Optimal Control
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DOI：
10.1051/cocv/2022060
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发表时间：
2020-09
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影响因子：
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通讯作者：
Riccardo Bonalli;T. Lew;M. Pavone
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文献类型：
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作者：
Riccardo Bonalli;T. Lew;M. Pavone
本文介绍了一个序列凸规划框架的非线性，有限维随机最优控制，其中的不确定性建模的多维维纳过程。我们证明了序列凸规划生成的迭代序列的任何聚点是一个候选局部最优解的意义下的随机庞特里亚金最大值原理的原问题。此外，我们提供了充分的条件，存在至少一个这样的聚点。然后，我们利用这些属性来设计一个实用的数值方法来解决非线性随机最优控制问题的基础上确定性转录的随机序列凸规划。
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.