Robustness and Consistency in Linear Quadratic Control with Predictions


We study the problem of learning-augmented predictive linear quadratic control. Our goal is to design a controller that balances consistency, which measures the competitive ratio when predictions are accurate, and robustness, which bounds the competitive ratio when predictions are inaccurate. We propose a novel $\lambda$-confident controller and prove that it maintains a competitive ratio upper bound of $1 + \min{O(\lambda^2 \epsilon) + O(1 - \lambda)^2, O(1) + O(\lambda^2)}$ where $\lambda \in [0, 1]$ is a trust parameter set based on the confidence in the predictions, and $\epsilon$ is the prediction error. Further, we design a self-tuning policy that adaptively learns the trust parameter $\lambda$ with a regret that depends on $\epsilon$ and the variation of perturbations and predictions.

Proceedings of the ACM on Measurement and Analysis of Computing Systems (SIGMETRICS 2022)
Chenkai Yu
Chenkai Yu
PhD Student in Decision, Risk, and Operations

Incentive, Information, and Computation