ReLU neural networks approximate transformed constraints in flat systems as unions of polytopes, enabling mixed-integer programming for guaranteed constraint satisfaction in CLF-based and MPC designs for nonlinear systems.
International Journal of Robust and Nonlinear Control: IFAC-Affiliated Journal13(3-4), 261–279 (2003)
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An ANN-Enhanced Approach for Flatness-Based Constrained Control of Nonlinear Systems
ReLU neural networks approximate transformed constraints in flat systems as unions of polytopes, enabling mixed-integer programming for guaranteed constraint satisfaction in CLF-based and MPC designs for nonlinear systems.