Fetching the paper…
Reading the bibliography…
We use Physics-Informed Neural Networks (PINNs) to solve the discrete-time nonlinear observer state estimation problem.
Observing the state of a linear system
D. Luenberger · 1963
Earlier work this paper cites.
Applied optimal estimation
A. Gelb et al · 1974
Earlier work this paper cites.
Linearization by output injection and nonlinear observers
A. J. Krener and A. Isidori · 1983
Earlier work this paper cites.
Sampled-data observer error linearization
S. T. Chung and J. W. Grizzle · 1990
Earlier work this paper cites.
Neural networks for self-learning control systems
D. H. Nguyen and B. Widrow · 1990
Earlier work this paper cites.
Observer design for autonomous discrete-time nonlinear systems
W. Lee and K. Nam · 1991
Earlier work this paper cites.
Neural networks for control systems—a survey
K. J. Hunt, D. Sbarbaro, R. Žbikowski, and P. J. Gawthrop · 1992
Earlier work this paper cites.
Observers for discrete-time nonlinear systems
G. Ciccarela, M. D. Mora, and A. Germani · 1993
Earlier work this paper cites.
Adaptive control of a class of nonlinear discrete-time systems using neural networks
Fu-Chuang Chen and H.K. Khalil · 1995
Earlier work this paper cites.
Universal approximation to nonlinear operators by neural networks with arbitrary activation functions and its application to dynamical systems
T. Chen and H. Chen · 1995
Earlier work this paper cites.
Nonlinear Control Systems
A. Isidori · 1995
Earlier work this paper cites.
Remarks on linearization of discrete-time autonomous systems and nonlinear observer design
W. Lin and C. I. Byrnes · 1995
Earlier work this paper cites.
Neural networks for control
W. T. Miller, R. S. Sutton, and P. J. Werbos · 1995
Earlier work this paper cites.
Application of neural networks to turbulence control for drag reduction
C. Lee, J. Kim, D. Babcock, and R. Goodman · 1997
Earlier work this paper cites.
Fuzzy control of a fluidized bed dryer
AV. Taprantzis, CI. Siettos, and GV. Bafas · 1997
Earlier work this paper cites.
Identification of distributed parameter systems: A neural net based approach
R. González-García, R. Rico-Martínez, and I. G. Kevrekidis · 1998
Earlier work this paper cites.
Nonlinear observer design using lyapunov’s auxiliary theorem
N. Kazantzis and C. Kravaris · 1998
Earlier work this paper cites.
On observer design for nonlinear discrete-time systems
T. Lilge · 1998
Cited alongside, same era.
Neural network output feedback control of robot manipulators
Y. H. Kim and F. L. Lewis · 1999
Cited alongside, same era.
Feedback Linearization, chapter 1,
A. J. Krener · 1999
Cited alongside, same era.
Observer-based adaptive fuzzy-neural control for unknown nonlinear dynamical systems
Y.G. Leu, T.T. Lee, and W.Y. Wang · 1999
Cited alongside, same era.
Advanced control strategies for fluidized bed dryers
CI. Siettos, CT. Kiranoudis, and GV. Bafas · 1999
Cited alongside, same era.
A reduced-order observer for nonlinear discrete-time systems
M. Boutayeb and M. Darouach · 2000
Cited alongside, same era.
Constructive Nonlinear Control
A. Sepulchre and M. Jankovic · 2011
Later among the works it cites.
Linear system theory and design
C. T. Chen · 2013
Later among the works it cites.
Deep learning for universal linear embeddings of nonlinear dynamics
B. Lusch, J. Nathan Kutz, and S. L. Brunton · 2018
Later among the works it cites.
Luenberger observers for discrete-time nonlinear systems
L. Brivadis, V. Andrieu, and U. Serres · 2019
Later among the works it cites.
Machine learning-based predictive control of nonlinear processes. part i: Theory
Z. Wu, A. Tran, D. Rincon, and P. D. Christofides · 2019
Later among the works it cites.
Machine-learning-based predictive control of nonlinear processes. part ii: Computational implementation
Z. Wu, A. Tran, D. Rincon, and P. D. Christofides · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
A functional equations approach to nonlinear discrete-time feedback stabilization through pole-placement
N. Kazantzis · 2001
Cited alongside, same era.
Discrete-time observer design using functional equations
N. Kazantzis and C. Kravaris · 2001
Cited alongside, same era.
Modelling of nonlinear process dynamics using kohonen’s neural networks, fuzzy systems and chebyshev series
AP. Alexandridis, CI. Siettos, HK. Sarimveis, AG. Boudouvis, and GV. Bafas · 2002
Cited alongside, same era.
Semiglobal stabilization of nonlinear systems using fuzzy control and singular perturbation methods
CI. Siettos and GV. Bafas · 2002
Cited alongside, same era.
Truncated chebyshev series approximation of fuzzy systems for control and nonlinear system identification
CI. Siettos, GV. Bafas, and AG. Boudouvis · 2002
Cited alongside, same era.
Functional observers with linear error dynamics for discrete-time nonlinear systems
S. Venkateswaran and C. Kravaris · 2002
Cited alongside, same era.
Real-time adaptive machine-learning-based predictive control of nonlinear processes
Z. Wu, D. Rincon, and P. D. Christofides · 2020
Later among the works it cites.
Machine-learning-based state estimation and predictive control of nonlinear processes
M. S. Alhajeri, Z. Wu, D. Rincon, F. Albalawi, and P. D. Christofides · 2021
Later among the works it cites.
Deep kkl: Data-driven output prediction for non-linear systems
S. Janny, V. Andrieu, M. N. Wolf, and C. Wolf · 2021
Later among the works it cites.
Dynamic input deep learning control of artificial avatars in a multi-agent joint motor task
M. Lombardi, D. Liuzza, and M. Di Bernardo · 2021
Later among the works it cites.
Using learning to control artificial avatars in human motor coordination tasks
M. Lombardi, D. Liuzza, and M. di Bernardo · 2021
Later among the works it cites.
Neural network-based kkl observer for nonlinear discrete-time systems
J. Peralez, M. Nadri, and D. Astolfi · 2022
Later among the works it cites.
Statistical machine-learning-based predictive control of uncertain nonlinear processes
Z. Wu, A. Alnajd, Q. Gu, and P. D. Christofides · 2022
Later among the works it cites.
Discrete-time nonlinear feedback linearization via physics-informed machine learning
H. Vargas Alvarez, G. Fabiani, N. Kazantzis, C. Siettos, and I. G. Kevrekidis · 2023
Later among the works it cites.
Learning robust state observers using neural odes
K. Miao and K. Gatsis · 2023
Later among the works it cites.
Data-driven control of agent-based models: An equation/variable-free machine learning approach
D. G. Patsatzis, L. Russo, I. G. Kevrekidis, and C. Siettos · 2023
Later among the works it cites.