Observing the state of networks with directed higher-order interactions
Provides a practical observer design for nonlinear networks with higher-order interactions, addressing a gap in existing methods.
This paper tackles state reconstruction in networks with directed higher-order interactions, proposing an algorithmic observer design that selects measurement nodes and gains. Numerical tests show robust performance, demonstrated by reconstructing agent opinions.
We consider the problem of reconstructing the state of a network of nonlinear dynamical systems in the presence of directed higher-order interactions. Grounded on analytical convergence results, we propose an algorithmic observer design procedure that simultaneously selects the nodes to be measured and the observer gains. We complement the theoretical analysis with an exhaustive numerical investigation campaign that showcases the performance and robustness of the designed observer. Finally, the algorithmic procedure is used to fully reconstruct the opinions of a group of agents.