{
  "id": "2210.10128",
  "version": "v1",
  "published": "2022-10-18T20:00:09.000Z",
  "updated": "2022-10-18T20:00:09.000Z",
  "title": "Distributed MPC for Self-Organized Cooperation of Multi-Agent Systems",
  "authors": [
    "Matthias Köhler",
    "Matthias A. Müller",
    "Frank Allgöwer"
  ],
  "categories": [
    "eess.SY",
    "cs.SY"
  ],
  "abstract": "We present a sequential distributed model predictive control (MPC) scheme suitable for cooperative control of multi-agent systems with dynamically decoupled heterogeneous nonlinear agents subject to individual constraints. We show that the cooperative goal is asymptotically fulfilled if a corresponding set and a suitable cost for cooperation are used, leading to a self-organized solution. Each agent solves an individual optimization problem for an artificial reference and an input that tracks it, only needing artificial references of its neighbors. The cost for cooperation couples these artificial references such that they are incrementally moved towards the cooperative goal. Since only artificial references are exchanged, communication is kept to a minimum. Furthermore, the scheme is easily extended to open systems, i.e., agents joining or leaving the multi-agent system. Finally, we apply the scheme to consensus and formation control in two examples.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2022-10-18T20:00:09.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "multi-agent system",
      "artificial reference",
      "distributed mpc",
      "heterogeneous nonlinear agents subject",
      "self-organized cooperation"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}