{
  "id": "2303.16581",
  "version": "v1",
  "published": "2023-03-29T10:29:47.000Z",
  "updated": "2023-03-29T10:29:47.000Z",
  "title": "Constraint-Adaptive MPC for linear systems: A system-theoretic framework for speeding up MPC through online constraint removal",
  "authors": [
    "S. A. N. Nouwens",
    "M. M. Paulides",
    "W. P. M. H. Heemels"
  ],
  "categories": [
    "math.OC",
    "cs.SY",
    "eess.SY"
  ],
  "abstract": "Reducing the computation time of model predictive control (MPC) is important, especially for systems constrained by many state constraints. In this paper, we propose a new online constraint removal framework for linear systems, for which we coin the term constraint-adaptive MPC (ca-MPC). In so-called exact ca-MPC, we adapt the imposed constraints by removing, at each time-step, a subset of the state constraints in order to reduce the computational complexity of the receding-horizon optimal control problem, while ensuring that the closed-loop behavior is {\\em identical} to that of the original MPC law. We also propose an approximate ca-MPC scheme in which a further reduction of computation time can be accomplished by a tradeoff with closed-loop performance, while still preserving recursive feasibility, stability, and constraint satisfaction properties. The online constraint removal exploits fast backward and forward reachability computations combined with optimality properties.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2023-03-29T10:29:47.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "linear systems",
      "constraint-adaptive mpc",
      "system-theoretic framework",
      "removal exploits fast backward",
      "online constraint removal exploits fast"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}