{
  "id": "1408.3259",
  "version": "v1",
  "published": "2014-08-14T12:18:35.000Z",
  "updated": "2014-08-14T12:18:35.000Z",
  "title": "Closed-loop control of an experimental mixing layer using machine learning control",
  "authors": [
    "Vladimir Parezanović",
    "Thomas Duriez",
    "Laurent Cordier",
    "Bernd R. Noack",
    "Joël Delville",
    "Jean-Paul Bonnet",
    "Marc Segond",
    "Markus Abel",
    "Steven L. Brunton"
  ],
  "categories": [
    "physics.flu-dyn"
  ],
  "abstract": "A novel framework for closed-loop control of turbulent flows is tested in an experimental mixing layer flow. This framework, called Machine Learning Control (MLC), provides a model-free method of searching for the best function, to be used as a control law in closed-loop flow control. MLC is based on genetic programming, a function optimization method of machine learning. In this article, MLC is benchmarked against classical open-loop actuation of the mixing layer. Results show that this method is capable of producing sensor-based control laws which can rival or surpass the best open-loop forcing, and be robust to changing flow conditions. Additionally, MLC can detect non-linear mechanisms present in the controlled plant, and exploit them to find a better type of actuation than the best periodic forcing.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2014-08-14T12:18:35.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "machine learning control",
      "closed-loop control",
      "control law",
      "detect non-linear mechanisms",
      "experimental mixing layer flow"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable",
      "adsabs": "2014arXiv1408.3259P"
    }
  }
}