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Koopman operator theory provides a powerful data-driven technique for modeling nonlinear dynamical systems in a linear framework, in comparison to computationally expensive and highly nonlinear physics-based simulations.
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M. T. Gillespie, C. M. Best, E. C. Townsend, D. Wingate, and M. D. Killpack, “Learning nonlinear dynamic models of soft robots for model predictive control with neural networks,” in 2018 IEEE International Conference on Soft Robotics (RoboSoft) , Apr. 2018, pp. 39–45
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2018
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D. Bruder, B. Gillespie, C. David Remy, and R. Vasudevan, “Modeling and Control of Soft Robots Using the Koopman Operator and Model Predictive Control,” Robotics: Science and Systems XV , Jun. 2019, conference Name: Robotics: Science and Systems 2019 ISBN: 9780992374754 Publisher: Robotics: Science and Systems Foundation
2019
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2021
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S. Tonkens, J. Lorenzetti, and M. Pavone, “Soft Robot Optimal Control Via Reduced Order Finite Element Models,” 2021 IEEE International Conference on Robotics and Automation (ICRA) , pp. 12 010–12 016, May 2021, conference Name: 2021 IEEE International Conference on Robotics and Automation (ICRA) ISBN: 9781728190778 Place: Xi’an, China Publisher: IEEE
2021
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D. Bruder, X. Fu, and R. Vasudevan, “Advantages of Bilinear Koopman Realizations for the Modeling and Control of Systems With Unknown Dynamics,” IEEE Robotics and Automation Letters , vol. 6, no. 3, pp. 4369–4376, Jul. 2021
2021
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D. Bruder, X. Fu, R. B. Gillespie, C. D. Remy, and R. Vasudevan, “Data-Driven Control of Soft Robots Using Koopman Operator Theory,” IEEE Transactions on Robotics , vol. 37, no. 3, pp. 948–961, Jun. 2021, conference Name: IEEE Transactions on Robotics
2021
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E. Kaiser, J. N. Kutz, and S. L. Brunton, “Data-driven discovery of Koopman eigenfunctions for control,” Machine Learning: Science and Technology , vol. 2, no. 3, p. 035023, Sep. 2021
2021
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2022
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D. A. Haggerty, M. J. Banks, E. Kamenar, A. B. Cao, P. C. Curtis, I. Mezić, and E. W. Hawkes, “Control of soft robots with inertial dynamics,” Science Robotics , vol. 8, no. 81, p. eadd6864, Aug. 2023, publisher: American Association for the Advancement of Science
2023
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L. Shi, Z. Liu, and K. Karydis, “Koopman Operators for Modeling and Control of Soft Robotics,” Current Robotics Reports , vol. 4, no. 2, pp. 23–31, Jun. 2023
2023
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X. Wang, Y. Cao, S. Chen, and Y. Kang, “Physics-informed deep Koopman operator for Lagrangian dynamic systems,” Science China Information Sciences , vol. 67, no. 9, p. 192201, Aug. 2024
2024
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