Learning-based robust model predictive control with data-driven Koopman operators
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Computer Vision and Pattern Recognition,Software
Link
https://link.springer.com/content/pdf/10.1007/s13042-023-01834-5.pdf
Reference58 articles.
1. Kamb M, Kaiser E, Brunton SL, Kutz JN (2020) Time-delay observables for Koopman: theory and applications. SIAM J Appl Dyn Syst 19(2):886–917. https://doi.org/10.1137/18M1216572
2. Koopman BO (1931) Hamiltonian systems and transformation in Hilbert space. Proc Natl Acad Sci 17(5):315–318. https://doi.org/10.1073/pnas.17.5.315
3. Koopman BO, Neumann JV (1932) Dynamical systems of continuous spectra. Proc Natl Acad Sci USA 18(3):255–263. https://doi.org/10.1073/pnas.18.3.255
4. Brunton SL, Brunton BW, Proctor JL, Nathan KJ, Kestler HA (2016) Koopman invariant subspaces and finite linear representations of nonlinear dynamical systems for control. PLoS ONE 11(2):0150171. https://doi.org/10.1371/journal.pone.0150171
5. Arbabi H, Korda M, Mezic I (2018) A data-driven Koopman model predictive control framework for nonlinear partial differential equations. In: 2018 IEEE Conference on Decision and Control (CDC), pp. 6409–6414. https://doi.org/10.1109/CDC.2018.8619720
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1. Model-based versus model-free optimal tracking for soft robots: analytical and data-driven Koopman modeling, control design and experimental validation;Nonlinear Dynamics;2024-06-21
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