An efficient data-based off-policy Q-learning algorithm for optimal output feedback control of linear systems

verfasst von

Mohammad Salahaldeen Ahmad Alsalti, Victor Gabriel Lopez Mejia, Matthias Müller

Abstract

In this paper, we present a Q-learning algorithm to solve the optimal output regulation problem for discrete-time LTI systems. This off-policy algorithm only relies on using persistently exciting input-output data, measured offline. No model knowledge or state measurements are needed and the obtained optimal policy only uses past input-output information. Moreover, our formulation of the proposed algorithm renders it computationally efficient. We provide conditions that guarantee the convergence of the algorithm to the optimal solution. Finally, the performance of our method is compared to existing algorithms in the literature.

Organisationseinheit(en)

Institut für Regelungstechnik

Typ

Aufsatz in Konferenzband

Seiten

312-323

Anzahl der Seiten

12

Publikationsdatum

14.07.2024

Publikationsstatus

Veröffentlicht

ASJC Scopus Sachgebiete

Artificial intelligence, Software, Steuerungs- und Systemtechnik, Statistik und Wahrscheinlichkeit

Elektronische Version(en)

https://doi.org/10.48550/arXiv.2312.03451 (Zugang: Offen)
https://proceedings.mlr.press/v242/alsalti24a.html (Zugang: Offen)