{
  "id": "1207.3178",
  "version": "v2",
  "published": "2012-07-13T09:23:01.000Z",
  "updated": "2014-05-05T09:45:59.000Z",
  "title": "Distributed MPC Via Dual Decomposition and Alternating Direction Method of Multipliers",
  "authors": [
    "Farhad Farokhi",
    "Iman Shames",
    "Karl H. Johansson"
  ],
  "comment": "Fixed Typos",
  "journal": "Distributed Distributed Model Predictive Control Made Easy (J. M. Maestre and R. R. Negenborn, eds.), Intelligent Systems, Control and Automation: Science and Engineering, 69, Springer, 2013",
  "categories": [
    "math.OC",
    "cs.SY"
  ],
  "abstract": "A conventional way to handle model predictive control (MPC) problems distributedly is to solve them via dual decomposition and gradient ascent. However, at each time-step, it might not be feasible to wait for the dual algorithm to converge. As a result, the algorithm might be needed to be terminated prematurely. One is then interested to see if the solution at the point of termination is close to the optimal solution and when one should terminate the algorithm if a certain distance to optimality is to be guaranteed. In this chapter, we look at this problem for distributed systems under general dynamical and performance couplings, then, we make a statement on validity of similar results where the problem is solved using alternating direction method of multipliers.",
  "revisions": [
    {
      "version": "v2",
      "updated": "2014-05-05T09:45:59.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "alternating direction method",
      "dual decomposition",
      "distributed mpc",
      "multipliers",
      "handle model predictive control"
    ],
    "tags": [
      "journal article"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable",
      "adsabs": "2012arXiv1207.3178F"
    }
  }
}