| Issue |
ESAIM: COCV
Volume 28, 2022
|
|
|---|---|---|
| Article Number | 64 | |
| Number of page(s) | 34 | |
| DOI | https://doi.org/10.1051/cocv/2022060 | |
| Published online | 20 October 2022 | |
Sequential convex programming for non-linear stochastic optimal control*
1
Université Paris-Saclay, CNRS, CentraleSupélec, Laboratoire des signaux et systèmes, 91190 Gif-sur-Yvette, France
2
Department of Aeronautics and Astronautics, Stanford University, Stanford, CA 94305, USA
** Corresponding author: riccardo.bonalli@centralesupelec.fr
Received:
9
November
2021
Accepted:
17
September
2022
This work introduces a sequential convex programming framework for non-linear, finitedimensional 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.
Mathematics Subject Classification: 49K40 / 65C30 / 93E20
Key words: Nonlinear stochastic optimal controlsequential convex programmingconvergence of Pontryagin extremal snumerical deterministic reformulation
© The authors. Published by EDP Sciences, SMAI 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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