{
  "id": "2308.07516",
  "version": "v1",
  "published": "2023-08-15T01:07:07.000Z",
  "updated": "2023-08-15T01:07:07.000Z",
  "title": "Robust Parameter Estimation for Hybrid Dynamical Systems",
  "authors": [
    "Ryan S. Johnson",
    "Stefano Di Cairano",
    "Ricardo G. Sanfelice"
  ],
  "categories": [
    "math.OC",
    "math.DS"
  ],
  "abstract": "We consider the problem of estimating a vector of unknown constant parameters for a class of hybrid dynamical systems -- that is, systems whose state variables exhibit both continuous (flow) and discrete (jump) evolution. Using a hybrid systems framework, we propose a hybrid estimation algorithm that can operate during both flows and jumps that, under a notion of hybrid persistence of excitation, guarantees convergence of the parameter estimate to the true value. Furthermore, we show that the parameter estimate is input-to-state stable with respect to a class of hybrid disturbances. Simulation results including a spacecraft application show the merits of our proposed approach.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2023-08-15T01:07:07.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "hybrid dynamical systems",
      "robust parameter estimation",
      "parameter estimate",
      "hybrid systems framework",
      "unknown constant parameters"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}