TY - DATA
T1 - Hybridization Toolbox for Model Predictive Control
PY - 2023/09/21
AU - Leila Gharavi
UR - 
DO - 10.4121/2a4a7bed-63b9-43d9-a4d2-192bc9163dd1.v1
KW - Hybridization Framework
KW - Model Predictive Control
KW - Evasive Maneuvers
KW - Vehicle Control
KW - Automated Driving
KW - Max-Min-Plus-Scaling Systems
KW - Hybrid Systems
KW - Computational Efficiency
KW - Hybrid Control
N2 - <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>
ER -