Data-Driven Static Output-Feedback Control for Multi-Agent Systems
Abstract
This work proposes a data-driven framework that synthesizes output-feedback control for linear time-invariant multi-agent systems (MAS). We first develop two algorithms that use offline data samples of state and input trajectories to determine a Nash equilibrium (NE) for MAS. Next, we formulate a semidefinite program that, given the NE solution, computes the static output-feedback controllers for the agents in MAS. Compared to prior work, virtues of our framework include weaker controllability assumptions, the absence of online information and data-driven estimation of states. Next, we use the proposed framework to design control schemes for voltage and frequency regulation for nonlinear multi-nodal electrical networks with grid-forming and grid-following inverters. Thus, compared to prior work that implements their data-driven frameworks in small-scale numerical examples, we validate the performance of our data-driven framework in high-fidelity nonlinear models of MAS. This implementation in realistic MAS provides insights that small-scale numerical examples may fail to capture. Results show successful grid stabilization and provision of regulation services from the inverters in the grid using our proposed framework. It can attain up to 78% less overshoot and 30% faster settling time than traditional strategies such as proportional-integral control.
Related
- Using artificial delays for prescribed/fixed-time static output feedback control of linear systems
- Static Output Feedback Control for Multi-Agent Systems Using Nash Equilibrium
- Using delays for prescribed-time static output feedback stabilization of LTI controllable and observable systems
- Adaptive Predefined-Time Output-Feedback Control of Uncertain Nonlinear Systems with Dead-Zone Actuation via Fractional-Power Feedback
- Model-Based Galerkin Lifting with Exact LTI Decomposition for Guaranteed ℋ∞ Output Feedback Control of Nonlinear Systems
BibTeX
@misc{sernatorre2026datadriven,
title = {{Data-Driven Static Output-Feedback Control for Multi-Agent Systems}},
author = {Paul Serna-Torre and Adam Sedlak and Patricia Hidalgo-Gonzalez},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.02623},
eprint = {2610.02623},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.02623},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - Data-Driven Static Output-Feedback Control for Multi-Agent Systems
AU - Paul Serna-Torre
AU - Adam Sedlak
AU - Patricia Hidalgo-Gonzalez
PY - 2026
DA - 2026/10/02
PB - arXiv
DO - 10.48550/arxiv.2610.02623
UR - https://arxiv.org/abs/2610.02623
AB - This work proposes a data-driven framework that synthesizes output-feedback control for linear time-invariant multi-agent systems (MAS). We first develop two algorithms that use offline data samples of state and input trajectories to determine a Nash equilibrium (NE) for MAS. Next, we formulate a semidefinite program that, given the NE solution, computes the static output-feedback controllers for the agents in MAS. Compared to prior work, virtues of our framework include weaker controllability assumptions, the absence of online information and data-driven estimation of states. Next, we use the proposed framework to design control schemes for voltage and frequency regulation for nonlinear multi-nodal electrical networks with grid-forming and grid-following inverters. Thus, compared to prior work that implements their data-driven frameworks in small-scale numerical examples, we validate the performance of our data-driven framework in high-fidelity nonlinear models of MAS. This implementation in realistic MAS provides insights that small-scale numerical examples may fail to capture. Results show successful grid stabilization and provision of regulation services from the inverters in the grid using our proposed framework. It can attain up to 78% less overshoot and 30% faster settling time than traditional strategies such as proportional-integral control.
ER -