Distributed Optimal Consensus Control for a Class of Uncertain Nonlinear Multiagent Networks With Disturbance Rejection Using Adaptive Technique

In this article, we consider the distributed optimal consensus problem under nominal and nonfragile cases for a class of minimum-phase uncertain nonlinear systems with unity-relative degree and disturbances generated by an external autonomous system. The involved cost function is the sum of all local cost functions associated with each individual agent. Two different edge-based distributed adaptive algorithms utilizing the internal model principle are designed to solve the problem in a fully distributed manner. Graph theory, nonsmooth analysis, convex analysis, and the Lyapunov theory are employed to show that the proposed algorithms converge accurately to the optimal solution of the considered problem. Finally, an example involving the dynamics of a Lorenz-type system is provided to demonstrate the effectiveness of the obtained results.

Automated summary

This article addresses the distributed optimal consensus problem for a class of minimum-phase uncertain nonlinear systems with unity-relative degree and external disturbances. Two edge-based distributed adaptive algorithms are proposed and shown to converge accurately to the optimal solution of the problem using graph theory, nonsmooth analysis, convex analysis, and Lyapunov theory. An example involving the dynamics of a Lorenz-type system is provided to demonstrate the effectiveness of the obtained results.