Distributed optimization for uncertain nonlinear interconnected multi-agent systems

Distributed optimization for uncertain nonlinear interconnected multi-agent systems is considered in this paper. The objective is to design distributed algorithms by using local information such that all the agents' outputs converge to the global optimal output. The problem is challenging due to unknown modeling uncertainties and nonlinear interconnections. First, a distributed algorithm composed of a distributed state observer and a distributed optimal output observer is presented for heterogeneous outputs. Next, it is shown that the objective can be achieved by employing solely a distributed optimal output observer if the agents' outputs are homogeneous. Finally, the algorithms' effectiveness is validated by an electrical power system composed of four-machine subsystems. (C) 2022 Elsevier B.V. All rights reserved.