A Model-Free Data-Driven Algorithm for Continuous-Time Control

TL;DR

This paper introduces a model-free, data-driven algorithm for designing optimal feedback controllers in continuous-time systems using only finite input-output data, applicable to linear and nonlinear systems.

Contribution

It proposes a novel algorithm that synthesizes infinite-horizon LQR controllers without requiring explicit system dynamics knowledge.

Findings

Successfully applied to linear systems

Extended to nonlinear air vehicle models

Demonstrates effectiveness with limited data

Abstract

Presented is an algorithm to synthesize an infinite-horizon LQR optimal feedback controller for continuous-time systems. The algorithm does not require knowledge of the system dynamics, but instead uses only a finite-length sampling of (possibly suboptimal) input-output data. The algorithm is based on a constrained optimization problem that enforces a necessary condition on the dynamics of the optimal value function along an arbitrary trajectory. This paper presents the derivation as well as shows examples applied to both linear and nonlinear systems inspired by air vehicles.