{
  "id": "1909.13398",
  "version": "v1",
  "published": "2019-09-29T23:51:50.000Z",
  "updated": "2019-09-29T23:51:50.000Z",
  "title": "Fractional-Order Model Predictive Control for Neurophysiological Cyber-Physical Systems: A Case Study using Transcranial Magnetic Stimulation",
  "authors": [
    "Orlando Romero",
    "Sarthak Chatterjee",
    "Sérgio Pequito"
  ],
  "comment": "Preprint submitted to ACC 2020",
  "categories": [
    "math.OC",
    "cs.SY",
    "eess.SY",
    "math.DS"
  ],
  "abstract": "Fractional-order dynamical systems are used to describe processes that exhibit temporal long-term memory and power-law dependence of trajectories. There has been evidence that complex neurophysiological signals like electroencephalogram (EEG) can be modeled by fractional-order systems. In this work, we propose a model-based approach for closed-loop Transcranial Magnetic Stimulation (TMS) to regulate brain activity through EEG data. More precisely, we propose a model predictive control (MPC) approach with an underlying fractional-order system (FOS) predictive model. Furthermore, MPC offers, by design, an additional layer of robustness to compensate for system-model mismatch, which the more traditional strategies lack. To establish the potential of our framework, we focus on epileptic seizure mitigation by computational simulation of our proposed strategy upon seizure-like events. We conclude by empirically analyzing the effectiveness of our method, and compare it with event-triggered open-loop strategies.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2019-09-29T23:51:50.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "fractional-order model predictive control",
      "neurophysiological cyber-physical systems",
      "case study",
      "fractional-order system",
      "closed-loop transcranial magnetic stimulation"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}