{
  "id": "2405.20182",
  "version": "v1",
  "published": "2024-05-30T15:49:26.000Z",
  "updated": "2024-05-30T15:49:26.000Z",
  "title": "Convergence Analysis for A Stochastic Maximum Principle Based Data Driven Feedback Control Algorithm",
  "authors": [
    "Siming Liang",
    "Hui Sun",
    "Richard Archibald",
    "Feng Bao"
  ],
  "comment": "arXiv admin note: text overlap with arXiv:2404.05734",
  "categories": [
    "math.OC",
    "cs.NA",
    "math.NA"
  ],
  "abstract": "This paper presents convergence analysis of a novel data-driven feedback control algorithm designed for generating online controls based on partial noisy observational data. The algorithm comprises a particle filter-enabled state estimation component, estimating the controlled system's state via indirect observations, alongside an efficient stochastic maximum principle type optimal control solver. By integrating weak convergence techniques for the particle filter with convergence analysis for the stochastic maximum principle control solver, we derive a weak convergence result for the optimization procedure in search of optimal data-driven feedback control. Numerical experiments are performed to validate the theoretical findings.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2024-05-30T15:49:26.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "stochastic maximum principle",
      "data driven feedback control algorithm",
      "convergence analysis",
      "data-driven feedback control",
      "principle type optimal"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}