TR2025-172
A dual ensemble Kalman filter approach to robust control of nonlinear systems: An application to partial differential equations
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- , "A dual ensemble Kalman filter approach to robust control of nonlinear systems: An application to partial differential equations", IEEE Conference on Decision and Control (CDC), December 2025.BibTeX TR2025-172 PDF
- @inproceedings{Joshi2025dec,
- author = {Joshi, Anant and Mowlavi, Saviz and Benosman, Mouhacine},
- title = {{A dual ensemble Kalman filter approach to robust control of nonlinear systems: An application to partial differential equations}},
- booktitle = {IEEE Conference on Decision and Control (CDC)},
- year = 2025,
- month = dec,
- url = {https://www.merl.com/publications/TR2025-172}
- }
- , "A dual ensemble Kalman filter approach to robust control of nonlinear systems: An application to partial differential equations", IEEE Conference on Decision and Control (CDC), December 2025.
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Abstract:
This paper considers the problem of data-driven robust control design for nonlinear systems, for instance, obtained when discretizing nonlinear partial differential equations (PDEs). A robust learning control approach is developed for nonlinear affine in control systems based on Lyapunov redesign technique. The robust control is developed as a sum of an optimal learning control which stabilizes the system in absence of disturbances, and an additive Lyapunov-based robustification term which handles the effects of disturbances. The dual ensemble Kalman filter (dual EnKF) algorithm is utilized in the optimal control design methodology. A simulation study is done on the heat equation and Burgers partial differential equation.
Related Publication
BibTeX arXiv
- @article{Joshi2025aug,
- author = {Joshi, Anant and Mowlavi, Saviz and Benosman, Mouhacine},
- title = {{A Dual Ensemble Kalman Filter Approach to Robust Control of Nonlinear Systems: An Application to Partial Differential Equations}},
- journal = {arXiv},
- year = 2025,
- month = aug,
- url = {https://arxiv.org/abs/2508.21684}
- }
