{
  "annotation_id": "5f00575e-f385-498c-a506-c5d4b2b94cc4",
  "date_created": "2026-02-17T05:53:11.996000Z",
  "date_modified": "2026-02-17T05:53:11.996000Z",
  "file_hash": "362be5293ea30b0ac482fd3d36d6ce95917e9e55382cef108f22028bb176fced",
  "private": false,
  "record": {
    "abstract": "This paper presents a method for estimating parameters that form a general model for human pilot response for specific tasks. The human model is essential for the dynamic analysis of piloted vehicles. Data are generated on a simulator with multiple trials being incorporated to find the single model that best describes the data. The model is found entirely in the time domain by constructing a constrained optimization problem. This optimization problem implicitly represents the state of the underlying system, making it robust to natural variation in human responses. It is demonstrated by estimating the human response model for a position control task with a quadcopter drone.",
    "arxiv_id": "2601.07956",
    "authors": [
      "Harrison M. Bonner",
      "Matthew R. Kirchner"
    ],
    "categories": [
      "eess.SY",
      "cs.SY",
      "math.OC"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "title": "Human as an Actuator Dynamic Model Identification",
    "url": "https://arxiv.org/abs/2601.07956",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "5dcf5828-a5de-41cf-87e4-71437aebd965",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}