Fetching the paper…
Reading the bibliography…
This paper considers the extension of data-enabled predictive control (DeePC) to nonlinear systems via general basis functions.
L. A. Zadeh, “From circuit theory to system theory,” Proceedings of the IRE , vol. 50, no. 5, pp. 856–865, 1962
1962
Earlier work this paper cites.
P. Eykhoff, System identification : parameter and state estimation . Wiley-Interscience, 1974
1974
Earlier work this paper cites.
M. Hanke and T. Raus, “A general heuristic for choosing the regularization parameter in ill-posed problems,” SIAM Journal of Scientific Computing , vol. 17, no. 4, pp. 956–972, 1996
1996
Earlier work this paper cites.
S.-S. Yang and C.-S. Tseng, “An orthogonal neural network for function approximation,” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) , vol. 26, no. 5, pp. 779–785, 1996
1996
Earlier work this paper cites.
W. Favoreel, B. De Moor, and M. Gevers, “SPC: Subspace predictive control,” in Proc. of the 14th IFAC World Congress , vol. 32, no. 2. Elsevier, 1999, pp. 4004–4009
1999
Earlier work this paper cites.
J. A. K. Suykens, T. Van Gestel, J. De Brabanter, B. De Moor, and J. Vandewalle, Least Squares Support Vector Machines . World Scientific, 2002
2002
Earlier work this paper cites.
J. C. Willems, P. Rapisarda, I. Markovsky, and B. L. De Moor, “A note on persistency of excitation,” Systems and Control Letters , vol. 54, no. 4, pp. 325–329, 2005
2005
Earlier work this paper cites.
S. A. Billings, Nonlinear System Identification: NARMAX Methods in the Time, Frequency, and Spatio–Temporal Domains . John Wiley & Sons, Inc. New York, 2013
2013
Earlier work this paper cites.
J. B. Rawlings, D. Q. Mayne, and M. M. Diehl, Model Predictive Control: Theory, Computation, and Design, 2nd Edition . Nob Hill Publishing, 2017
2017
Earlier work this paper cites.
M. Korda and I. Mezić, “Linear predictors for nonlinear dynamical systems: Koopman operator meets model predictive control,” Automatica , vol. 93, pp. 149–160, 2018
2018
Cited alongside, same era.
J. Coulson, J. Lygeros, and F. Dörfler, “Data-Enabled Predictive Control: In the Shallows of the DeePC,” in 18th European Control Conference , Napoli, Italy, 2019, pp. 307–312
2019
Cited alongside, same era.
J. Berberich and F. Allgöwer, “A trajectory-based framework for data-driven system analysis and control,” in 19th European Control Conference , Saint Petersburg, Russia, 2020, pp. 1365–1370
2020
Cited alongside, same era.
D. Masti, F. Smarra, A. D’Innocenzo, and A. Bemporad, “Learning affine predictors for MPC of nonlinear systems via artificial neural networks,” IFAC-PapersOnLine , vol. 53, no. 2, pp. 5233–5238, 2020
2020
Cited alongside, same era.
Y. Lian and C. N. Jones, “Nonlinear data-enabled prediction and control,” in Proceedings of the 3rd Conference on Learning for Dynamics and Control , ser. Proceedings of Machine Learning Research, A. Jadbabaie, J. Lygeros, G. J. Pappas, P. A. Parrilo, B. Recht, C. J. Tomlin, and M. N. Zeilinger, Eds., vol. 144. PMLR, 2021, pp. 523–534
2021
Later among the works it cites.
Y. Lian, R. Wang, and C. N. Jones, “Koopman based data-driven predictive control,” arXiv , vol. 2102.05122, 2021
2021
Later among the works it cites.
F. Dörfler, J. Coulson, and I. Markovsky, “Bridging direct & indirect data-driven control formulations via regularizations and relaxations,” IEEE Transactions on Automatic Control , 2022
2022
Later among the works it cites.
J. Berberich, J. Köhler, M. A. Müller, and F. Allgöwer, “Linear Tracking MPC for Nonlinear Systems—Part II: The Data-Driven Case,” IEEE Transactions on Automatic Control , vol. 67, no. 9, pp. 4406–4421, 2022
2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
M. Korda and I. Mezić, Koopman Model Predictive Control of Nonlinear Dynamical Systems . Cham: Springer International Publishing, 2020, pp. 235–255
2020
Cited alongside, same era.
J. Berberich, J. Köhler, M. A. Müller, and F. Allgöwer, “On the design of terminal ingredients for data-driven MPC,” IFAC–PapersOnLine , vol. 54, no. 6, pp. 257–263, 2021, 7th IFAC Conference on Nonlinear Model Predictive Control NMPC 2021
2021
Cited alongside, same era.
F. Fiedler and S. Lucia, “On the relationship between data–enabled predictive control and subspace predictive control,” in IEEE Proc. of the European Control Conference (ECC) , Rotterdam, The Netherlands, 2021, pp. 222–229
2021
Cited alongside, same era.
E. Elokda, J. Coulson, P. N. Beuchat, J. Lygeros, and F. Dörfler, “Data-enabled predictive control for quadcopters,” International Journal of Robust and Nonlinear Control , vol. 31, pp. 8916–8936, 2021
2021
Cited alongside, same era.
C. Verhoek, H. S. Abbas, R. Tóth, and S. Haesaert, “Data-driven predictive control for linear parameter-varying systems,” IFAC-PapersOnLine , vol. 54, no. 8, pp. 101–108, 2021, 4th IFAC Workshop on Linear Parameter Varying Systems LPVS 2021
2021
Cited alongside, same era.
L. Huang, J. Lygeros, and F. Dörfler, “Robust and kernelized data-enabled predictive control for nonlinear systems,” arXiv , vol. 2206.01866, 2022
2022
Later among the works it cites.
G. Pillonetto, T. Chen, A. Chiuso, G. De Nicolao, and L. L., Regularized System Identification , ser. Communications and Control Engineering. Springer Cham, 2022
2022
Later among the works it cites.
A. Dalla Libera and G. Pillonetto, “Deep prediction networks,” Neurocomputing , vol. 469, pp. 321–329, 2022
2022
Later among the works it cites.
M. Alsalti, V. G. Lopez, J. Berberich, F. Allgöwer, and M. A. Müller, “Data-driven nonlinear predictive control for feedback linearizable systems,” arXiv , vol. 2211.06339, 2023
2023
Closest in time.
A. Fazzi and A. Chiuso, “Data-driven prediction and control for NARX systems,” arXiv , vol. 2304.02930, 2023
2023
Closest in time.