IEEE Open Journal of Control Systems · 2023
Certifying Black-Box Policies With Stability for Nonlinear Control
IEEE Open Journal of Control Systems
How can model based advice stabilize a learned nonlinear policy?
Simply blending two stabilizing policies can cause instability. An adaptive confidence policy instead uses approximate model information to certify stability, with competitive guarantees under bounded nonlinearity and evaluations on Cart Pole and charging under distribution shift.
Research themes
Connections
From prediction error to control performance
Trust adaptation in linear quadratic control and perturbation bounds for MPC connect the quality of a forecast to the cost of acting on it. Nonlinear certification adds stability to this picture.
Cite this paper
Tongxin Li, Ruixiao Yang, Guannan Qu, Yiheng Lin, Adam Wierman, Steven H. Low. Certifying Black-Box Policies With Stability for Nonlinear Control. IEEE Open Journal of Control Systems, 2023. https://doi.org/10.1109/OJCSYS.2023.3241486
BibTeX
@article{tongxin-certified-policies,
title = {{Certifying Black-Box Policies With Stability for Nonlinear Control}},
author = {Tongxin Li and Ruixiao Yang and Guannan Qu and Yiheng Lin and Adam Wierman and Steven H. Low},
year = {2023},
journal = {IEEE Open Journal of Control Systems},
url = {https://doi.org/10.1109/OJCSYS.2023.3241486},
doi = {10.1109/OJCSYS.2023.3241486}
}
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