Sequential convex programming for non-linear stochastic optimal control
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DOI10.1051/COCV/2022060zbMATH Open1500.49014arXiv2009.05182OpenAlexW3084558633MaRDI QIDQ5043060
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Publication date: 27 October 2022
Published in: (Search for Journal in Brave)
Abstract: 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.
Full work available at URL: https://arxiv.org/abs/2009.05182
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