TY - GEN
T1 - Data-Based Modeling and Control of Dynamical Systems
T2 - 60th IEEE Conference on Decision and Control, CDC 2021
AU - Gueho, Damien
AU - Majji, Manoranjan
AU - Singla, Puneet
N1 - Funding Information:
ACKNOWLEDGMENT The third author wishes to acknowledge Texas A & M Institute of Data Sciences (TAMIDS) Career Initiation Fellowship program for their support. Partial support of the National Geospatial Intelligence Agency (Grant No. HM0476-19-1-2015) and the Office of Naval Research (Grant No. N00014-19-1-2435) is gratefully acknowledged. The results reported in sensitivity analysis techniques are part of collaborative research with Sandia National Laboratories. Mr. Michael Wang of Texas A & M University and Dr. Anup Parikh of Sandia National Laboratories are acknowledged for posing the parameter sensitivity problem and helping the second author develop the approaches discussed here.
Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Parameter estimation methods to provide data-based models to control complex dynamical systems are reviewed. Starting from least square minimization of the equation error, the tutorial provides an overview of how different perspectives of parameter estimation lead to various algorithms that are used in diverse contexts. Both statistical and deterministic approaches are discussed, and the utility of model inferences are explained. The discussions provide a context and review relevant background with respect to three application papers involving recent advances in Gaussian Process Regression (GPR), state estimation approaches and data-driven modeling.
AB - Parameter estimation methods to provide data-based models to control complex dynamical systems are reviewed. Starting from least square minimization of the equation error, the tutorial provides an overview of how different perspectives of parameter estimation lead to various algorithms that are used in diverse contexts. Both statistical and deterministic approaches are discussed, and the utility of model inferences are explained. The discussions provide a context and review relevant background with respect to three application papers involving recent advances in Gaussian Process Regression (GPR), state estimation approaches and data-driven modeling.
UR - http://www.scopus.com/inward/record.url?scp=85126026860&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85126026860&partnerID=8YFLogxK
U2 - 10.1109/CDC45484.2021.9682951
DO - 10.1109/CDC45484.2021.9682951
M3 - Conference contribution
AN - SCOPUS:85126026860
T3 - Proceedings of the IEEE Conference on Decision and Control
SP - 31
EP - 36
BT - 60th IEEE Conference on Decision and Control, CDC 2021
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 13 December 2021 through 17 December 2021
ER -