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Robust and optimal predictive control of the COVID-19 outbreak, , , , , and . Annual reviews in control, (2020)arXiv preprint arXiv:2005.03580.Bounding the difference between model predictive control and neural networks, , , , and . Learning for Dynamics and Control Conference, page 817--829. PMLR, (2022)Neural network training under semidefinite constraints, , , , and . 2022 IEEE 61st Conference on Decision and Control (CDC), page 2731-2736. IEEE, (2022)Robustness analysis and training of recurrent neural networks using dissipativity theory, , and . Automatisierungstechnik, 70 (8): 730-739 (2022)Convolutional Neural Networks as 2-D systems, , , , and . (March 2023)Bounding the difference between model predictive control and neural networks, , , , and . Learning for Dynamics and Control Conference, 168, page 817-829. PMLR, (2022)Smartphone Apps for Learning Progress and Course Revision, , and . 21st IFAC World Congress, 53, 2, page 17368-17373. Elsevier, (2020)Sharing economy and optimal investment decisions for distributed solar generation, , , , , and . Applied energy, (2021)Offset-free setpoint tracking using neural network controllers, , , , and . Proceedings of the 3rd Conference on Learning for Dynamics and Control, 144, page 992-1003. PMLR, (2021)Facilitating learning progress in a first control course via Matlab apps, , , and . 21st IFAC World Congress, 53, 2, page 17356-17361. Elsevier, (2020)