Robust Consensus for Descriptor Multi-agent Systems with Uncertainties in all Matrices

this paper discusses robust consensus problems for Descriptor Multi-agent Systems (DMASs) with constant topologies. The agents are depicted using generic linear descriptor systems. We consider the uncertainties with norm bounded-ness in perturbations of derivative matrix
$E$
and in other system matrices including
$A$
and B. Also, each agent only can share the information of the output with the neighboring agents. Applying robust control theory in descriptor linear system, necessary and sufficient conditions for existence of dynamic compensators are derived to figure out the consensus problem. The problem of robust consensus is transferred into the question of a feasible solution for linear matrix inequality. Dynamical compensators, based on robust algorithm, are proposed to achieve consensus. Finally, numerical instances are presented to demonstrate efficacy and merits of main yields.