Bounded consensus in second-order uncertain nonlinear multiagent systems: A distributed neural network control approach

Abstract

This paper explores the consensus issue in second-order generalized nonlinear multiagent systems (MAS) that involve uncertain nonlinear dynamics and external disturbances from the system. Suppose that the uncertain nonlinear terms can be approximated by neural networks with nonlinear residues. Through the incorporation of localized adaptive observer and disturbance observer for each agent, we propose a distributed adaptive protocol to promote bounded consensus (BC) in the network. Due to the use of saturated functions and adaptive gains in the protocol, the chattering phenomenon is weakened. Finally, two simulation examples verify the results of the fully distributed adaptive bounded consensus protocol.