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This paper presents a novel learning framework to construct Koopman eigenfunctions for unknown, nonlinear dynamics using data gathered from experiments.
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J. L. Proctor, S. L. Brunton, and J. Nathan Kutz, “Generalizing Koopman Theory to Allow for Inputs and Control *,” vol. 17, no. 1, pp. 909–930, 2018
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2016
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E. Kaiser, J. N. Kutz, and S. L. Brunton, “Data-driven discovery of Koopman eigenfunctions for control,” no. April, 2017
2017
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E. Kaiser, J. N. Kutz, and S. L. Brunton, “Sparse identification of nonlinear dynamics for model predictive control in the low-data limit,” Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences
2018
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S. L. Brunton, J. L. Proctor, and J. Nathan Kutz, “Discovering governing equations from data by sparse identification of nonlinear dynamical systems,”
Cited in the paper.
2019
Closest in time.
A. J. Taylor, V. D. Dorobantu, H. M. Le, Y. Yue, and A. D. Ames, “Episodic Learning with Control Lyapunov Functions for Uncertain Robotic Systems,” 2019
2019
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R. Cheng, G. Orosz, R. M. Murray, and J. W. Burdick, “End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Tasks,” in Proc. AAAI
2019
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No. April, 2019
S. Brunton and N. Kutz, Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control · 2019
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C. Folkestad, D. Pastor, and J. Burdick, “Episodic Koopman Learning of Nonlinear Robot Dynamics with Application to Fast Quadrotor Landing,” in Proc. International Conference of Robotics and Autonomy
2020
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