Resource allocation for high-order multiagent systems with uncertainties from non-neighboring nodes

This paper studies the distributed resource allocation problems of high-order multiagent systems with nonlinear uncertainties. The nonlinear uncertainties consist of the external time-varying disturbances, the uncertain dynamics of agents, and the interferences from the neighboring and non-neighboring nodes of agents. To accomplish the distributed resource allocation under various uncertainties, this paper proposes a new algorithm based on actively estimating and compensating for the lumped uncertainty. By considering the output-feedback situation, the algorithm is constructed based on a full-order extended state observer that offers the estimates of lumped uncertainty and unmeasured states. For the uncertainties with nonlinear growth rates, the convergence analysis of the proposed algorithm is given. The proposed theoretical results illustrate that the resource allocation task can be practically achieved with a tunable optimization error. Finally, numerical simulations show the effectiveness of the proposed algorithms.