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We consider two problems related to disturbance attenuation in undirected consensus networks of identical linear systems subject to exogenous disturbances: 1) network interconnection design and 2) design of distributed and decentralized controllers. We use the \cal H\infty norm of the transfer function from the disturbance vector to the disagreement vector of the network as the performance metric for disturbance attenuation. We show that the disturbance attenuation performance is enhanced by maximizing the second smallest eigenvalue of the graph Laplacian under a certain condition, which can be checked using a linear matrix inequality. For the case of a consensus network with fixed interconnection weights, e.g., as the result of physical constraints, we provide algorithms for the design of both decentralized and distributed controllers that ensure a prescribed disturbance attenuation performance. [ABSTRACT FROM AUTHOR]

This note presents a robust iterative learning controller design method for plants subject to interval model uncertainty in the A-matrix of their state-space description. First-order perturbation theory is used to find bounds on the eigenvalues and eigenvectors of the powers of A when A is an interval matrix. These bounds are then used for calculation of the interval uncertainty of the Markov matrix. The bounds on the Markov matrix are then used to design an iterative learning controller that ensures monotonic convergence for all systems in the interval plant. [ABSTRACT FROM AUTHOR]