POMACS · 2022
Robustness and Consistency in Linear Quadratic Control with Untrusted Predictions
Proceedings of the ACM on Measurement and Analysis of Computing Systems
Can predictive control balance consistency and robustness automatically?
A confidence parameter governs how much a linear quadratic controller trusts disturbance predictions. Competitive bounds describe the tradeoff between accurate and inaccurate advice, and a self tuning policy adapts confidence online using observed prediction quality.
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, Guanya Shi, Chenkai Yu, Adam Wierman, Steven Low. Robustness and Consistency in Linear Quadratic Control with Untrusted Predictions. Proceedings of the ACM on Measurement and Analysis of Computing Systems, 2022. https://doi.org/10.1145/3508038
BibTeX
@article{tongxin-robust-lqc,
title = {{Robustness and Consistency in Linear Quadratic Control with Untrusted Predictions}},
author = {Tongxin Li and Ruixiao Yang and Guannan Qu and Guanya Shi and Chenkai Yu and Adam Wierman and Steven Low},
year = {2022},
journal = {Proceedings of the ACM on Measurement and Analysis of Computing Systems},
url = {https://doi.org/10.1145/3508038},
doi = {10.1145/3508038}
}
Other versions
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