A Data-Driven Algorithm for Model-Free Control Synthesis

TL;DR

This paper introduces a data-driven algorithm that synthesizes optimal infinite-horizon LQR controllers for continuous-time systems using only finite input-output data, without requiring explicit system models.

Contribution

It proposes a novel model-free control synthesis method based on constrained optimization, enabling the calculation of both feedback and feedforward gains for reference tracking.

Findings

Successfully synthesizes LQR controllers from data without system identification.

Validates the approach on real aircraft data, demonstrating practical applicability.

Provides theoretical justification for the data-driven control method.

Abstract

Presented is an algorithm to synthesize the optimal infinite-horizon LQR 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 arbitrary 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 any trajectory. In addition to calculating the standard LQR gain matrix, a feedforward gain can be found to implement a reference tracking controller. This paper presents a theoretical justification for the method and shows several examples, including a validation test on a real scale aircraft.