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.
IEEE Transactions on Industrial Electronics 64(5), 4076–4090 (2016)
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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.