[34] Andreas Doerr, Duy Nguyen-Tuong, Alonso Marco, Stefan Schaal, and Sebastian Trimpe. Model-based policy search for automatic tuning of multivariate pid controllers. In 2017 IEEE International Conference on Robotics and Automation (ICRA), pages 5295-5301. IEEE, 2017.
[35] Ignacio Carlucho, Mariano De Paula, and Gerardo G Acosta. An adaptive deep reinforcement learning approach for mimo pid control of mobile robots. ISA transactions, 102:280-294, 2020.
[36] Joseph Sill. Monotonic networks. In Proceedings of the 10th International Conference on Neural Information Processing Systems, pages 661-667, 1997.
[37] Antoine Wehenkel and Gilles Loupe. Unconstrained monotonic neural networks. Advances in neural information processing systems, 32, 2019.
[38] Xingchao Liu, Xing Han, Na Zhang, and Qiang Liu. Certified monotonic neural networks. Advances in Neural Information Processing Systems, 33:15427-15438, 2020.
[39] Hennie Daniels and Marina Velikova. Monotone and partially monotone neural networks. IEEE Transactions on Neural Networks, 21(6):906-917, 2010.
[40] Aishwarya Sivaraman, Golnoosh Farnadi, Todd Millstein, and Guy Van den Broeck. Counterexample-guided learning of monotonic neural networks. Advances in Neural Information Processing Systems, 33:11936-11948, 2020.
[41] Hong Zhang and Zhen Zhang. Feedforward networks with monotone constraints. In IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No. 99CH36339), volume 3, pages 1820-1823. IEEE, 1999.
[42] Ernest K Ryu and Stephen Boyd. Primer on monotone operator methods. Appl. comput. math, 15(1):3-43, 2016.
[43] Anusha Nagabandi, Gregory Kahn, Ronald S Fearing, and Sergey Levine. Neural network dynamics for model-based deep reinforcement learning with model-free fine-tuning. In 2018 IEEE International Conference on Robotics and Automation (ICRA), pages 7559-7566. IEEE, 2018.
[44] Lucian Buşoniu, Tim de Bruin, Domagoj Tolic, Jens Kober, and Ivana Palunko. Reinforcement learning for control: Performance, stability, and deep approximators. Annual Reviews in Control, 46:8-28, 2018.
[45] Priya L Donti, Melrose Roderick, Mahyar Fazlyab, and J Zico Kolter. Enforcing robust control guarantees within neural network policies. In International Conference on Learning Representations, 2020.
[46] Pulkit Nahata, Raffaele Soloperto, Michele Tucci, Andrea Martinelli, and Giancarlo Ferrari-Trecate. A passivity-based approach to voltage stabilization in dc microgrids with zip loads. Automatica, 113:108770, 2020.
[47] Chris Meissen, Kristian Klausen, Murat Arcak, Thor I Fossen, and Andrew Packard. Passivity-based formation control for uavs with a suspended load. IFAC-PapersOnLine, 50(1):13150-13155, 2017.
[48] Wei He, Hejia Gao, Chen Zhou, Chenguang Yang, and Zhijun Li. Reinforcement learning control of a flexible two-link manipulator: an experimental investigation. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51(12):7326-7336, 2020.
[49] Brandon Amos, Lei Xu, and J Zico Kolter. Input convex neural networks. In International Conference on Machine Learning, pages 146-155. PMLR, 2017.
[50] Yize Chen, Yuanyuan Shi, and Baosen Zhang. Optimal control via neural networks: A convex approach. In International Conference on Learning Representations, 2019.
[51] Stephen Boyd and Lieven Vandenberghe. Convex Optimization. Cambridge University Press, 2004.