Distributed adaptive neural network consensus control of fractional-order multi-agent systems with unknown control directions

Abstract

In this work, adaptive consensus control of leader-following fractional-order multi-agent systems whose each subsystem includes functional uncertainties, external disturbances, and unknown control directions is investigated utilizing neural networks and the Nussbaum function. The controller is synthesized within the framework of the backstepping algorithm, where the “explosion of complexity” problem is mitigated through the use of a command filter, and the adverse impact of filtered errors is decreased using compensation signals. The function uncertainties of each follower are approximated by neural networks, and a disturbance update law is developed to identify the boundary of the disturbance. Importantly, a general conclusion is provided to affirm the applicability of the Nussbaum function in addressing controller design for fractional-order systems with unknown control directions. Finally, the validity of the proposed approach is verified via two numerical examples.