{
  "annotation_id": "7e3e45dc-112a-44d4-86cd-0bda05b5532b",
  "date_created": "2026-02-17T05:53:12.010000Z",
  "date_modified": "2026-02-17T05:53:12.010000Z",
  "file_hash": "100e9b65f2fe2790374f43e6a60e342e6e65b5247c406fdf245c65cb7d35668c",
  "private": false,
  "record": {
    "abstract": "This work advances the maximum hands-off sparse control framework by developing a robust counterpart for constrained linear systems with parametric uncertainties. The resulting optimal control problem minimizes an $L^{0}$ objective subject to an uncountable, compact family of constraints, and is therefore a nonconvex, nonsmooth robust optimization problem. To address this, we replace the $L^{0}$ objective with its convex $L^{1}$ surrogate and, using a nonsmooth variant of the robust Pontryagin maximum principle, show that the $L^{0}$ and $L^{1}$ formulations have identical sets of optimal solutions -- we call this the robust hands-off principle. Building on this equivalence, we propose an algorithmic framework -- drawing on numerically viable techniques from the semi-infinite robust optimization literature -- to solve the resulting problems. An illustrative example is provided to demonstrate the effectiveness of the approach.",
    "arxiv_id": "2601.07256",
    "authors": [
      "Siddhartha Ganguly",
      "Kenji Kashima"
    ],
    "categories": [
      "math.OC",
      "cs.NA",
      "cs.RO",
      "cs.SY",
      "eess.SY",
      "math.NA"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "title": "Robust maximum hands-off optimal control: existence, maximum principle, and $L^{0}$-$L^1$ equivalence",
    "url": "https://arxiv.org/abs/2601.07256",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "1fb35ea6-7912-426a-b929-da8c9710ec8d",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}