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This paper demonstrates the benefits of imposing stability on data-driven Koopman operators.
B. O. Koopman, “ Hamiltonian systems and transformation in Hilbert space ,” Proceedings of the National Academy of Sciences , vol. 17, no. 5, pp. 315–318, 1931
1931
Earlier work this paper cites.
A. J. Pritchard and J. Zabczyk, “Stability and stabilizability of infinite-dimensional systems,” Siam Review , vol. 23, no. 1, pp. 25–52, 1981
1981
Earlier work this paper cites.
J. Yuh, “Modeling and control of underwater robotic vehicles,” IEEE Transactions on Systems, man, and Cybernetics , vol. 20, no. 6, pp. 1475–1483, 1990
1990
Earlier work this paper cites.
R. Tibshirani, “Regression shrinkage and selection via the lasso,” Journal of the Royal Statistical Society: Series B (Methodological) , vol. 58, no. 1, pp. 267–288, 1996
1996
Earlier work this paper cites.
R. Johansson, A. Robertsson, K. Nilsson, and M. Verhaegen, “State-space system identification of robot manipulator dynamics,” Mechatronics , vol. 10, no. 3, pp. 403–418, 2000
2000
Earlier work this paper cites.
N. Borovykh and M. Spijker, “Resolvent conditions and bounds on the powers of matrices, with relevance to numerical stability of initial value problems,” Journal of Computational and Applied Mathematics , vol. 125, no. 1-2, pp. 41–56, 2000
2000
Earlier work this paper cites.
N. Roy and A. McCallum, “Toward optimal active learning through monte carlo estimation of error reduction,” ICML, Williamstown , pp. 441–448, 2001
2001
Earlier work this paper cites.
B. Klaassen, R. Linnemann, D. Spenneberg, and F. Kirchner, “Biomimetic walking robot scorpion: Control and modeling,” Robotics and autonomous systems , vol. 41, no. 2-3, pp. 69–76, 2002
2002
Earlier work this paper cites.
C. Dima, M. Hebert, and A. Stentz, “Enabling learning from large datasets: Applying active learning to mobile robotics,” in IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA’04. 2004 , vol. 1. IEEE, 2004, pp. 108–114
2004
Earlier work this paper cites.
J. C. Kinsey, R. M. Eustice, and L. L. Whitcomb, “A survey of underwater vehicle navigation: Recent advances and new challenges,” in IFAC Conference of Manoeuvering and Control of Marine Craft , vol. 88, 2006, pp. 1–12
2006
Earlier work this paper cites.
P. J. Antsaklis and A. N. Michel, Linear systems . Springer Science & Business Media, 2006
2006
Earlier work this paper cites.
J. Swevers, W. Verdonck, and J. De Schutter, “Dynamic model identification for industrial robots,” IEEE control systems magazine , vol. 27, no. 5, pp. 58–71, 2007
2007
Earlier work this paper cites.
G. Kan-feng and Z. Ming-yang, “Dynamic modeling and simulation of driving control for wheeled mobile robot on sand,” Journal of System Simulation , vol. 20, no. 18, pp. 5035–5039, 2008
2008
Earlier work this paper cites.
B. Boots, G. J. Gordon, and S. M. Siddiqi, “A constraint generation approach to learning stable linear dynamical systems,” in Advances in neural information processing systems , 2008, pp. 1329–1336
2008
Earlier work this paper cites.
U. Muico, Y. Lee, J. Popović, and Z. Popović, “Contact-aware nonlinear control of dynamic characters,” in ACM SIGGRAPH 2009 papers , 2009, pp. 1–9
2009
Earlier work this paper cites.
P. J. Schmid, “Dynamic mode decomposition of numerical and experimental data,” Journal of fluid mechanics , vol. 656, pp. 5–28, 2010
2010
Earlier work this paper cites.
W. M. Haddad and V. Chellaboina, Nonlinear dynamical systems and control: a Lyapunov-based approach . Princeton university press, 2011
2011
Earlier work this paper cites.
G. Droge and M. Egerstedt, “Adaptive time horizon optimization in model predictive control,” in Proceedings of the 2011 American Control Conference . IEEE, 2011, pp. 1843–1848
2011
Earlier work this paper cites.
M. Budišić, R. Mohr, and I. Mezić, “ Applied Koopmanism ,” Chaos , vol. 22, no. 4, p. 047510, 2012
2012
Earlier work this paper cites.
G. Halikias, L. Dritsas, A. Pantelous, and V. Tsoulkas, “Strong stability of discrete-time systems,” Linear Algebra and its Applications , vol. 436, no. 7, pp. 1890–1908, 2012
2012
Earlier work this paper cites.
Y. F. Zheng, H. Wang, S. Li, Y. Liu, D. Orin, K. Sohn, Y. Jun, and P. Oh, “Humanoid robots walking on grass, sands and rocks,” in 2013 IEEE Conference on Technologies for Practical Robot Applications (TePRA) . IEEE, 2013, pp. 1–6
2013
Earlier work this paper cites.
A. Baranes and P.-Y. Oudeyer, “Active learning of inverse models with intrinsically motivated goal exploration in robots,” Robotics and Autonomous Systems , vol. 61, no. 1, pp. 49–73, 2013
2013
Earlier work this paper cites.
I. Mezić, “Analysis of fluid flows via spectral properties of the koopman operator,” Annual Review of Fluid Mechanics , vol. 45, pp. 357–378, 2013
2013
Earlier work this paper cites.
Y. Lan and I. Mezić, “ Linearization in the large of nonlinear systems and Koopman operator spectrum ,” Physica D: Nonlinear Phenomena , vol. 242, no. 1, pp. 42–53, 2013
2013
Earlier work this paper cites.
I. Mezić, “ On applications of the spectral theory of the Koopman operator in dynamical systems and control theory ,” in Proceedings of the Conference on Decision and Control , 2015, pp. 7034–7041
2015
Earlier work this paper cites.
M. O. Williams, I. G. Kevrekidis, and C. W. Rowley, “ A data-driven approximation of the Koopman operator: Extending dynamic mode decomposition ,” Journal of Nonlinear Science , vol. 25, no. 6, pp. 1307–1346, 2015
2015
Earlier work this paper cites.
G. Mamakoukas, M. A. MacIver, and T. D. Murphey, “Sequential action control for models of underactuated underwater vehicles in a planar ideal fluid,” in 2016 American Control Conference (ACC) . IEEE, 2016, pp. 4500–4506
2016
Earlier work this paper cites.
S. L. Brunton, J. L. Proctor, and J. N. Kutz, “ Discovering governing equations from data by sparse identification of nonlinear dynamical systems ,” Proceedings of the National Academy of Sciences , vol. 113, no. 15, pp. 3932–3937, 2016
2016
Earlier work this paper cites.
W. He, W. Ge, Y. Li, Y.-J. Liu, C. Yang, and C. Sun, “Model identification and control design for a humanoid robot,” IEEE Transactions on Systems, Man, and Cybernetics: Systems , vol. 47, no. 1, pp. 45–57, 2016
2016
Earlier work this paper cites.
A. Mauroy and J. Goncalves, “ Linear identification of nonlinear systems: A lifting technique based on the Koopman operator ,” in Proceedings of the Conference on Decision and Control , 2016, pp. 6500–6505
2016
Earlier work this paper cites.
S. L. Brunton, B. W. Brunton, J. L. Proctor, and J. N. Kutz, “ Koopman invariant subspaces and finite linear representations of nonlinear dynamical systems for control ,” PloS One , vol. 11, no. 2, p. e0150171, 2016
2016
Earlier work this paper cites.
B. W. Brunton, L. A. Johnson, J. G. Ojemann, and J. N. Kutz, “Extracting spatial–temporal coherent patterns in large-scale neural recordings using dynamic mode decomposition,” Journal of neuroscience methods , vol. 258, pp. 1–15, 2016
2016
Cited alongside, same era.
2016
Cited alongside, same era.
W.-b. Huang, L. le Cao, F. Sun, D. Zhao, H. Liu, and S. Yu, “Learning stable linear dynamical systems with the weighted least square method.” in IJCAI , 2016, pp. 1599–1605
2016
Cited alongside, same era.
I. Abraham, G. De La Torre, and T. D. Murphey, “Model-based control using koopman operators,” Proceedings of Robotics: Science and Systems , 2017
2017
Cited alongside, same era.
2019
Later among the works it cites.
2019
Later among the works it cites.
S. Sinha, U. Vaidya, and E. Yeung, “On computation of Koopman operator from sparse data,” in 2019 American Control Conference (ACC) . IEEE, 2019, pp. 5519–5524
2019
Later among the works it cites.
S. Sinha, B. Huang, and U. Vaidya, “On robust computation of Koopman operator and prediction in random dynamical systems,” Journal of Nonlinear Science , pp. 1–34, 2019
2019
Later among the works it cites.
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2017
Cited alongside, same era.
2017
Cited alongside, same era.
N. Takeishi, Y. Kawahara, and T. Yairi, “ Learning Koopman invariant subspaces for dynamic mode decomposition ,” in Proceedings of the Neural Information Processing Systems , 2017, pp. 1130–1140
2017
Cited alongside, same era.
H. Arbabi and I. Mezic, “Ergodic theory, dynamic mode decomposition, and computation of spectral properties of the koopman operator,” SIAM Journal on Applied Dynamical Systems , vol. 16, no. 4, pp. 2096–2126, 2017
2017
Cited alongside, same era.
2017
Cited alongside, same era.
A. M. Boudali, P. J. Sinclair, R. Smith, and I. R. Manchester, “Human locomotion analysis: Identifying a dynamic mapping between upper and lower limb joints using the koopman operator,” in 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) . IEEE, 2017, pp. 1889–1892
2017
Cited alongside, same era.
P. Shcherbakov, “On peak effects in discrete time linear systems,” in 2017 25th Mediterranean Conference on Control and Automation (MED) . IEEE, 2017, pp. 376–381
2017
Cited alongside, same era.
M. Bauza and A. Rodriguez, “A probabilistic data-driven model for planar pushing,” in 2017 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2017, pp. 3008–3015
2017
Cited alongside, same era.
D. Bruder, C. D. Remy, and R. Vasudevan, “Nonlinear system identification of soft robot dynamics using koopman operator theory,” in 2019 International Conference on Robotics and Automation (ICRA) . IEEE, 2019, pp. 6244–6250
2019
Later among the works it cites.
N. Gillis, M. Karow, and P. Sharma, “Approximating the nearest stable discrete-time system,” Linear Algebra and its Applications , vol. 573, pp. 37–53, 2019
2019
Later among the works it cites.
C. Gaz, M. Cognetti, A. Oliva, P. R. Giordano, and A. De Luca, “Dynamic identification of the franka emika panda robot with retrieval of feasible parameters using penalty-based optimization,” IEEE Robotics and Automation Letters , vol. 4, no. 4, pp. 4147–4154, 2019
2019
Later among the works it cites.
G. Mamakoukas, O. Xherija, and T. D. Murphey, “Memory-efficient learning of stable linear dynamical systems for prediction and control,” Neural Information Processing Systems (NeurIPS) , 2020
2020
Closest in time.
M. L. Castaño, A. Hess, G. Mamakoukas, T. Gao, T. Murphey, and X. Tan, “Control-oriented modeling of soft robotic swimmer with koopman operators,” in International Conference on Advanced Intelligent Mechatronics (AIM) , 2020
2020
Closest in time.
C. Folkestad, Y. Chen, A. D. Ames, and J. W. Burdick, “Data-driven safety-critical control: Synthesizing control barrier functions with koopman operators,” IEEE Control Systems Letters , 2020
2020
Closest in time.
——, “A note on approximating the nearest stable discrete-time descriptor systems with fixed rank,” Applied Numerical Mathematics , vol. 148, pp. 131–139, 2020
2020
Closest in time.
F. R. Hogan and A. Rodriguez, “Feedback control of the pusher-slider system: A story of hybrid and underactuated contact dynamics,” in Algorithmic Foundations of Robotics XII . Springer, 2020, pp. 800–815
2020
Closest in time.
C.-V. Pal and F. Leon, “Brief survey of model-based reinforcement learning techniques,” in 2020 24th International Conference on System Theory, Control and Computing (ICSTCC) . IEEE, 2020, pp. 92–97
2020
Closest in time.
M. Korda and I. Mezić, “Optimal construction of koopman eigenfunctions for prediction and control,” IEEE Transactions on Automatic Control , vol. 65, no. 12, pp. 5114–5129, 2020
2020
Closest in time.
O. Azencot, N. B. Erichson, V. Lin, and M. Mahoney, “Forecasting sequential data using consistent koopman autoencoders,” in International Conference on Machine Learning . PMLR, 2020, pp. 475–485
2020
Closest in time.
A. A. Ahmadi and B. El Khadir, “Learning dynamical systems with side information,” in Learning for Dynamics and Control . PMLR, 2020, pp. 718–727
2020
Closest in time.
2020
Closest in time.
G. Mamakoukas, M. L. Castano, X. Tan, and T. D. Murphey, “Derivative-based koopman operators for real-time control of robotic systems,” IEEE Transactions on Robotics , 2021
2021
Closest in time.
2021
Closest in time.
M. Haseli and J. Cortes, “Data-driven approximation of koopman-invariant subspaces with tunable accuracy,” in Annual American Control Conference (ACC) . IEEE, 2021
2021
Closest in time.
2021
Closest in time.
H. Lange, S. L. Brunton, and J. N. Kutz, “From fourier to koopman: Spectral methods for long-term time series prediction.” J. Mach. Learn. Res. , vol. 22, pp. 41–1, 2021
2021
Closest in time.
J. Drgoňa, A. R. Tuor, V. Chandan, and D. L. Vrabie, “Physics-constrained deep learning of multi-zone building thermal dynamics,” Energy and Buildings , vol. 243, p. 110992, 2021
2021
Closest in time.
K. Kashinath, M. Mustafa, A. Albert, J. Wu, C. Jiang, S. Esmaeilzadeh, K. Azizzadenesheli, R. Wang, A. Chattopadhyay, A. Singh et al. , “Physics-informed machine learning: case studies for weather and climate modelling,” Philosophical Transactions of the Royal Society A , vol. 379, no. 2194, p. 20200093, 2021
2021
Closest in time.
2021
Closest in time.
2021
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2021
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2021
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H. Yin, P. Seiler, M. Jin, and M. Arcak, “Imitation learning with stability and safety guarantees,” IEEE Control Systems Letters , 2021
2021
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