%0 Computer Program
%A Gharavi, Leila
%D 2023
%T Hybridization Toolbox for Model Predictive Control
%U 
%R 10.4121/2a4a7bed-63b9-43d9-a4d2-192bc9163dd1.v1
%K Hybridization Framework
%K Model Predictive Control
%K Evasive Maneuvers
%K Vehicle Control
%K Automated Driving
%K Max-Min-Plus-Scaling Systems
%K Hybrid Systems
%K Computational Efficiency
%K Hybrid Control
%X <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>
%I 4TU.ResearchData