{
  "id": "2009.05182",
  "version": "v1",
  "published": "2020-09-11T00:27:23.000Z",
  "updated": "2020-09-11T00:27:23.000Z",
  "title": "Sequential Convex Programming For Non-Linear Stochastic Optimal Control",
  "authors": [
    "Riccardo Bonalli",
    "Thomas Lew",
    "Marco Pavone"
  ],
  "categories": [
    "math.OC",
    "cs.SY",
    "eess.SY"
  ],
  "abstract": "We introduce a sequential convex programming framework to solve general non-linear stochastic optimal control problems in finite dimension, where uncertainties are modeled by a multidimensional Wiener process. We provide sufficient conditions for the convergence of the method. Moreover, we prove that, when convergence is achieved, sequential convex programming finds a candidate locally optimal solution for the original problem in the sense of the stochastic Pontryagin Maximum Principle. We leverage those properties to design a practical numerical method to solve non-linear stochastic optimal control problems that is based on a deterministic transcription of stochastic sequential convex programming.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2020-09-11T00:27:23.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "sequential convex programming",
      "non-linear stochastic optimal control problems",
      "general non-linear stochastic optimal control",
      "stochastic pontryagin maximum principle"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}