Consensus Control of a Class of Uncertain Nonlinear Multiagent Systems via Gradient-Based Algorithms

This paper is concerned with the distributed optimization problem for a class of minimum-phase nonlinear multiagent systems with second relative degree. The target is to minimize a cost function composed of a group of convex local functions of the outputs in a cooperative manner. Novel state feedback control laws are first proposed and the consensus subject to the optimization constraint is achieved when the communication graph is directed. By replacing the derivatives of the outputs in the state feedback control laws with their estimations, the output feedback control laws are constructed and proved to achieve exponential consensus under the undirected graph. The effectiveness of the proposed control laws is validated by some simulations.