Dec 18, 2019 · We study model-free learning methods for the output-feedback Linear Quadratic (LQ) control problem in finite-horizon subject to subspace constraints on the ...

We study model-free learning methods for the output-feedback Linear Quadratic (LQ) control prob- lem in finite-horizon subject to subspace constraints on the ...

It is proved that a fundamental class of distributed control problems - commonly referred to as Quadratically Invariant (QI) problems - as well as others ...

May 30, 2020 · Abstract. We study model-free learning methods for the output-feedback Linear Quadratic (LQ) control prob- lem in finite-horizon subject to ...

Learning the globally optimal distributed LQ regulator · An Input-Output Parametrization of Stabilizing Controllers: amidst Youla and System Level Synthesis.

Furieri, Luca, Yang Zheng, and Maryam Kamgarpour. “Learning the globally optimal distributed LQ regulator.” Learning for Dynamics and Control. PMLR, 2020. [ ...

Sep 27, 2019 · Learning the Globally Optimal Distributed LQ Regulator ... We study model-free learning methods for the output-feedback Linear Quadratic (LQ) ...

Dec 16, 2024 · We propose an online learning algorithm that adaptively designs a decentralized linear quadratic regulator when the system model is unknown a priori.

Learning the Globally Optimal Distributed LQ Regulator. Luca Furieri, Yang ... Robust Deep Learning as Optimal Control: Insights and Convergence Guarantees.

[1] Furieri, Zheng, Kamgarpour, “Learning the globally optimal distributed LQ regulator”, L4DC, 2020. The is nonconvex in distributed policies . However ...