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Review on Modeling and Control of Flexible Link Manipulators

Dipendra Subedi, Ilya Tyapin, Geir Hovland
2020 Modeling, Identification and Control  
A survey of the reported studies is carried out based on the method used for modeling link flexibility and obtaining equations of motion of the FLMs.  ...  The merits and limitations of different modeling and control methods are highlighted.  ...  Acknowledgments The work was funded by SFI Offshore Mechatronics, project number 237896.  ... 
doi:10.4173/mic.2020.3.2 fatcat:jscokhufqjbo7f5gaclkbykgra

Modeling and adaptive control for a spatial flexible spacecraft with unknown actuator failures

Zhijie Liu, Zhiji Han, Zhijia Zhao, Wei He
2021 Science China Information Sciences  
In this paper, we address simultaneous control of a flexible spacecraft's attitude and vibrations in a three-dimensional space under input disturbances and unknown actuator failures.  ...  Using Hamilton's principle, the system dynamics is modeled as an infinite dimensional system captured using partial differential equations.  ...  The system is described by coupled PDEs and ODEs. When the panel deflections are zero, the system can be described as a well-known dynamic model of a rigid spacecraft.  ... 
doi:10.1007/s11432-020-3109-x fatcat:2nhsto4ggrfptlx6x4xkb6vqma

On Aronsson Equation and Deterministic Optimal Control

Pierpaolo Soravia
2008 Applied Mathematics and Optimization  
The methodology uses a Piecewise Linear (PWL) approximation of the Ordinary Differential Equations (ODEs) vector field which describes the dynamics of a system parameterized by the control inputs in order  ...  In the present study, we assume that the system satisfies the locally Lipschitz condition and the Lipschitz constant is estimated a priori.  ...  Day Virginia Tech day@math.vt.edu CP4 Natural Observers for Second-Order Bilinear Infinite-Dimensional Systems We consider the observer design for a class of second order bilinear infinite dimensional  ... 
doi:10.1007/s00245-008-9048-7 fatcat:kihxqtczfzdn5pbmvsd42axbk4

Derivative-informed projected neural network for large-scale Bayesian optimal experimental design [article]

Keyi Wu, Thomas O'Leary-Roseberry, Peng Chen, Omar Ghattas
2022 arXiv   pre-print
, smoothness, and intrinsic low-dimensionality of the map using a small and dimension-independent number of PDE solves.  ...  We address the solution of large-scale Bayesian optimal experimental design (OED) problems governed by partial differential equations (PDEs) with infinite-dimensional parameter fields.  ...  The PDE models can be extremely expensive to solve for each realization of the infinite-dimensional uncertain parameters.  ... 
arXiv:2201.07925v1 fatcat:rro3otg4fne4tkkfftd3to7cd4

Estimation of Heat Source Term and Thermal Diffusion in Tokamak Plasmas Using a Kalman Filtering Method in the Early Lumping Approach

Sarah Mechhoud, Emmanuel Witrant, Luc Dugard, Didier Moreau
2015 IEEE Transactions on Control Systems Technology  
The Extended Kalman Filter with Unknown Inputs Without Direct Feed-through (EKF-UI-WDF) is applied to estimate simultaneously the unknown parameters and inputs and an adaptive fading memory coefficient  ...  In this paper, early lumping estimation of spacetime varying diffusion coefficient and source term for a nonhomogeneous linear parabolic partial differential equation (PDE) describing Tokamak plasma heat  ...  EARLY LUMPING APPROACH FOR THE JOINT DIFFUSION AND INPUT ESTIMATION In early lumping approaches, the PDE is first converted into a finite dimensional system and then an estimation method is used to recover  ... 
doi:10.1109/tcst.2014.2342760 fatcat:4ihve2wdczdvxj6zwsjydabnzm

Bayesian Neural Ordinary Differential Equations [article]

Raj Dandekar, Karen Chung, Vaibhav Dixit, Mohamed Tarek, Aslan Garcia-Valadez, Krishna Vishal Vemula, Chris Rackauckas
2022 arXiv   pre-print
Subsequently, for the first time, we demonstrate the successful integration of variational inference with normalizing flows and Neural ODEs, leading to a powerful Bayesian Neural ODE object.  ...  Together, this gives a scientific machine learning tool for probabilistic estimation of epistemic uncertainties.  ...  By noticing that in the limit of infinite layers, a ResNet module [15] behaves as a continuous time ODE, Neural ODEs allow the arXiv:2012.07244v4 [cs.LG] 6 Feb 2022 coupling of neural networks as expressive  ... 
arXiv:2012.07244v4 fatcat:rmlwv5ywknch3mignxbc5hciwq

Learning physics-based models from data: perspectives from inverse problems and model reduction

Omar Ghattas, Karen Willcox
2021 Acta Numerica  
of the input–output map through approximation in a low-dimensional subspace.  ...  In inverse problems, we seek to infer uncertain components of the inputs from observations of the outputs, while in model reduction we seek low-dimensional models that explicitly capture the salient features  ...  Acknowledgements We gratefully acknowledge the numerous contributions of students, postdoctoral researchers, research scientists and collaborators who have contributed to the work described here.  ... 
doi:10.1017/s0962492921000064 fatcat:olerbfrqqvfi5m33txfwblzltu

Deep learning for spatio-temporal forecasting – application to solar energy [article]

Vincent Le Guen
2022 arXiv   pre-print
performances and parameter identification.  ...  We show that differentiable shape and temporal criteria can be leveraged to improve the performances of existing models.  ...  Remerciements Je tiens à remercier ici toutes les personnes qui ont concouru à l'achèvement du travail présenté dans ce manuscrit.  ... 
arXiv:2205.03571v1 fatcat:dwkprkwf6ncgjcnvkpx3yrdfjm

State and Parameter Estimation for Natural Gas Pipeline Networks Using Transient State Data

Kaarthik Sundar, Anatoly Zlotnik
2018 IEEE Transactions on Control Systems Technology  
We consider a state estimation problem that is then extended to a joint state and parameter estimation problem that can be used for data assimilation.  ...  In both formulations, the flow dynamics are described on each pipe by space- and time-dependent density and mass flux that evolve according to a system of coupled partial differential equations, in which  ...  For the subsequent estimation computations, we utilize a finite difference approximation for the derivatives in the ODE/DAE system (in Eq. (24)/(21)) and convert the nonlinear system of ODEs/DAEs to a  ... 
doi:10.1109/tcst.2018.2851507 fatcat:q42iu7ixfvbernct4h4y5ouwgy

2019 Index IEEE Transactions on Automatic Control Vol. 64

2019 IEEE Transactions on Automatic Control  
., TAC July 2019 3046-3053 2019 4188-4195 Adaptive filters Online Maximum-Likelihood Estimation of the Parameters of Partially Observed Diffusion Processes.  ...  ., +, TAC Feb. 2019 841-846 Output Feedback Boundary Control of a Heat PDE Sandwiched Between Two ODEs.  ... 
doi:10.1109/tac.2020.2967132 fatcat:o2hd2t4jz5fbpkcemjt5aj7xrm

Learning hydrodynamic equations for active matter from particle simulations and experiments [article]

Rohit Supekar, Boya Song, Alasdair Hastewell, Gary P. T. Choi, Alexander Mietke, Jörn Dunkel
2021 arXiv   pre-print
This inference framework makes it possible to measure a large number of hydrodynamic parameters in parallel and directly from video data.  ...  In parallel, data-driven algorithms for learning interpretable continuum models have shown promising potential for the recovery of underlying partial differential equations (PDEs) from continuum simulation  ...  Neglecting multiplicative noise and factorizing paircorrelations gives rise to a nonlinear integro-differential equation [46, 48] that can be transformed into an infinite hierarchy of coupled PDEs for  ... 
arXiv:2101.06568v3 fatcat:wtabs4j3efbyvn6othh7i47w4m

2019 Index IEEE Transactions on Fuzzy Systems Vol. 27

2019 IEEE transactions on fuzzy systems  
Boulkroune, A., +, TFUZZ Sept. 2019 1703-1713 Fuzzy Observer Based Control for Nonlinear Coupled Hyperbolic PDE-ODE Systems.  ...  ., +, TFUZZ Jan. 2019 172-184 Fuzzy Observer Based Control for Nonlinear Coupled Hyperbolic PDE-ODE Systems.  ...  Nonlinear filters Adaptive Neuro-Fuzzy Control for Discrete-Time Nonaffine Nonlinear Systems. Gil  ... 
doi:10.1109/tfuzz.2020.2966828 fatcat:pgfo5oksjrdbpa5s534ky74bie

A Moment-Matching Method to Study the Variability of Phenomena Described by Partial Differential Equations

Jean-Frédéric Gerbeau, Damiano Lombardi, Eliott Tixier
2018 SIAM Journal on Scientific Computing  
The proposed approach is illustrated with elliptic and parabolic PDEs. In the Appendix, an nonlinear ODE is considered and the strategy is compared with two existing ones.  ...  Given an adequate model, it is possible to account for this variability by allowing some parameters to adopt a stochastic behavior.  ...  Acknowledgements This research was supported by a French Ministry of Higher Education and Research grant.  ... 
doi:10.1137/16m1103476 fatcat:2jn455pu7jcdhpz2tbvf4pivo4

Multilayer perceptrons and radial basis function neural network methods for the solution of differential equations: A survey

Manoj Kumar, Neha Yadav
2011 Computers and Mathematics with Applications  
Our main purpose is to provide a synthesis of the published research works in this area and stimulate further research interest and effort in the identified topics.  ...  In this paper, we present a wide survey and classification of different Multilayer Perceptron (MLP) and Radial Basis Function (RBF) neural network techniques, which are used for solving differential equations  ...  Acknowledgments The authors extend their appreciation to anonymous reviewers for their valuable suggestions in revising this paper.  ... 
doi:10.1016/j.camwa.2011.09.028 fatcat:snzxuw2pjngnli3bi35zigspva

Estimating parameters with pre-specified accuracies in distributed parameter systems using optimal experiment design

M. G. Potters, X. Bombois, M. Mansoori, Paul M. J. Van den Hof
2016 International Journal of Control  
We show how to adapt the classical framework for these systems and take into account scaling and stability issues.  ...  Our methodology is then applied to a relevant problem in heat transfer studies: estimation of conductivity and diffusivity parameters in front-face experiments.  ...  Acknowledgments The authors acknowledge Prof. Rashtchian of Sharif University of Technology, Iran, for supporting the assignment of M. Mansoori to TU Delft.  ... 
doi:10.1080/00207179.2016.1138143 fatcat:24eml6i62rb2vf2uw3zvchldzy
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