Stability of Certainty-Equivalent Adaptive LQR for Linear Systems with Unknown Time-Varying Parameters

TL;DR

This paper introduces a stable, adaptive control method for linear systems with unknown, time-varying parameters, combining classical estimation and control techniques with proven stability guarantees.

Contribution

It presents a modular, computationally efficient adaptive LQR approach with stability analysis for systems with changing dynamics and disturbances.

Findings

Proven finite-gain ll^2-stability of the closed-loop system.

The method is effective on a nonlinear quadrotor simulation.

Combines least mean square filter with certainty-equivalent LQR seamlessly.

Abstract

Standard model-based control design deteriorates when the system dynamics change during operation. To overcome this challenge, online and adaptive methods have been proposed in the literature. In this work, we consider the class of discrete-time linear systems with unknown time-varying parameters. We propose a simple, modular, and computationally tractable approach by combining two classical and well-known building blocks from estimation and control: the least mean square filter and the certainty-equivalent linear quadratic regulator. Despite both building blocks being simple and off-the-shelf, our analysis shows that they can be seamlessly combined to a powerful pipeline with stability guarantees. Namely, finite-gain $\ell^2$-stability of the closed-loop interconnection of the unknown system, the parameter estimator, and the controller is proven, despite the presence of unknown disturbances and time-varying parametric uncertainties. Real-world applicability of the proposed algorithm is showcased by simulations carried out on a nonlinear planar quadrotor.