Robustness and Consistency in Linear Quadratic Control with Untrusted Predictions

We study the problem of learning-augmented predictive linear quadratic control. Our goal is to design a controller that balances \textit{"consistency"}, which measures the competitive ratio when predictions are accurate, and \textit{"robustness"}, which bounds the competitive ratio when predictions are inaccurate. We propose a novel \(\lambda\)-confident policy and provide a competitive ratio upper bound that depends on a trust parameter \(\lambda\in [0,1]\) set based on the confidence in the predictions and some prediction error \(\varepsilon\). Motivated by online learning methods, we design a self-tuning policy that adaptively learns the trust parameter \(\lambda\) with a competitive ratio that depends on \(\varepsilon\) and the variation of system perturbations and predictions. We show that its competitive ratio is bounded from above by \( 1+{O(\varepsilon)}/({{\Theta(1)+\Theta(\varepsilon)}})+O(\mu_{\mathsf{Var}})\) where \(\mu_\mathsf{Var}\) measures the variation of perturbations and predictions. It implies that when the variations of perturbations and predictions are small, by automatically adjusting the trust parameter online, the self-tuning scheme ensures a competitive ratio that does not scale up with the prediction error \(\varepsilon\).