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arXiv · 2610.02623

Data-Driven Static Output-Feedback Control for Multi-Agent Systems

Paul Serna-Torre·Adam Sedlak·Patricia Hidalgo-Gonzalez
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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.

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Paul Serna-Torre, Adam Sedlak, Patricia Hidalgo-Gonzalez. 2026-10-02. Data-Driven Static Output-Feedback Control for Multi-Agent Systems. https://arxiv.org/abs/2610.02623

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