Distributed Adaptive Output Consensus for High-Order Multiagent Systems with Input Saturation and Uncertain Nonlinear Dynamics

Abstract

This paper deals with the leader-following output consensus problem for a class of high-order affine nonlinear strict-feedback multiagent systems with unknown control gains and input saturation under a general directed graph. Nussbaum gain function technique is used to handle the unknown control gains, and the uncertain nonlinear dynamics of each agent is approximated by radial basis function neural networks. Distributed adaptive controllers are designed via the backstepping technique as well as the dynamic surface control approach. It is proved that the closed-loop multiagent systems are semiglobally uniformly ultimately bounded, and the output consensus error can converge to a small region around the origin. Finally, the theoretical results are supported by a numerical simulation.

Investigating Nonlinear Consensus Interaction in Multi-Agent Systems: Exploring Symmetry and Asymmetry

“…The mathematical consensus model has its roots in de Groot's first linear model that was designed for applications in economics. Researchers have presented several mathematical scenarios to solve the consensus problem, such as the nonlinear model [9]- [14], the linear model [15], leader-follow [16], first-order [17], second-order [18], highorder [19], and event-based models for switched and fixed topology in directed and undirected agents' networks [20].…”