Data-Driven cooperative output regulation of continuous-Time multi-Agent systems with unknown network topology

This paper investigates data-driven cooperative output regulation for continuous-time multi-agent systems with unknown network topology. Unlike existing studies that typically assume a known network topology to directly compute controller parameters, a novel approach is proposed that allows for the computation of the parameters without prior knowledge of the topology. A lower bound on the minimum non-zero eigenvalue of the Laplacian matrix is estimated using only edge weight bounds, enabling the output regulation controller design to be independent of global network information. This approach is applicable to both directed and undirected graphs. Additionally, the common need for state derivative measurements is eliminated, reducing the amount of data requirements. Furthermore, necessary and sufficient conditions are established to ensure that the data are informative for cooperative output regulation, leading to the design of a distributed controller. In the presence of noisy data, an upper bound on the output error is derived, which increases with the noise level. A distributed controller is then designed to realize approximate cooperative output regulation. Finally, the effectiveness of the methods is verified through numerical simulations of the unmanned vehicle swarm. © 2025 The Franklin Institute.