Regret-Optimal LQR Control

TL;DR

This paper introduces a regret-optimal controller for infinite-horizon LQR problems, minimizing worst-case regret against a clairvoyant controller with future disturbance knowledge, and provides explicit formulas for its design.

Contribution

It formulates a novel regret-based criterion for LQR control, deriving explicit solutions that interpolate between $H_2$ and $H_$ optimal controllers.

Findings

The regret-optimal controller is linear and explicitly constructed from standard LQR solutions.

Simulations show the controller balances $H_2$ and $H_$ costs effectively.

Explicit formulas for the optimal regret and controller are derived via a Nehari extension approach.

Abstract

We consider the infinite-horizon LQR control problem. Motivated by competitive analysis in online learning, as a criterion for controller design we introduce the dynamic regret, defined as the difference between the LQR cost of a causal controller (that has only access to past disturbances) and the LQR cost of the \emph{unique} clairvoyant one (that has also access to future disturbances) that is known to dominate all other controllers. The regret itself is a function of the disturbances, and we propose to find a causal controller that minimizes the worst-case regret over all bounded energy disturbances. The resulting controller has the interpretation of guaranteeing the smallest regret compared to the best non-causal controller that can see the future. We derive explicit formulas for the optimal regret and for the regret-optimal controller for the state-space setting. These explicit solutions are obtained by showing that the regret-optimal control problem can be reduced to a Nehari extension problem that can be solved explicitly. The regret-optimal controller is shown to be linear and can be expressed as the sum of the classical $H_2$ state-feedback law and an $n$-th order controller ($n$ is the state dimension), and its construction simply requires a solution to the standard LQR Riccati equation and two Lyapunov equations. Simulations over a range of plants demonstrate that the regret-optimal controller interpolates nicely between the $H_2$ and the $H_\infty$ optimal controllers, and generally has $H_2$ and $H_\infty$ costs that are simultaneously close to their optimal values. The regret-optimal controller thus presents itself as a viable option for control systems design.