cff-version: 1.2.0
abstract: "<p>This toolbox can be used to hybridize any nonlinear function given as its input argument, which can be either a nonlinear prediction model or the nonlinear function expressing the boundary of the feasible region, i.e. the nonlinear constraints.</p><p><br></p><p>A grid is generated on the function domain and the toolbox returns the hybridized form of the nonlinear function. The user can select the type and form of approximation based on the problem type:</p><p><br></p><ul><li>For <strong>model approximation</strong>, the options are</li></ul><ol><li class="ql-indent-1"><em>selecting the grid type</em> and</li><li class="ql-indent-1"><em>specifying the number of affine modes in the MMPS formulation</em>.</li></ol><ul><li>For <strong>constraint approximation</strong>, the options are</li></ul><ol><li class="ql-indent-1"><em>specifying the number of subregions</em>,</li><li class="ql-indent-1"><em>selecting between polytopic (MMPS-based) or ellipsoidal approximation</em>, and</li><li class="ql-indent-1"><em>choosing between boundary-based or region-based approximation</em>.</li></ol>"
authors:
  - family-names: Gharavi
    given-names: Leila
    orcid: "https://orcid.org/0000-0003-0301-3504"
title: "Hybridization Toolbox for Model Predictive Control"
keywords:
version: 1
identifiers:
  - type: doi
    value: 10.4121/2a4a7bed-63b9-43d9-a4d2-192bc9163dd1.v1
license: MIT
date-released: 2023-09-21