Distributed consensus tracking control of second‐order non‐linear multi‐agent systems with unmodelled dynamics

Abstract

In this study, the consensus tracking control problem is considered for a class of second-order non-linear multi-agent systems with unmodelled dynamics. Radial basis function neural networks are introduced to approximate the unknown non-linearities and a dynamic signal is utilised to deal with the unmodelled dynamics. Meanwhile, appropriate Lyapunov functions are constructed and a new distributed adaptive control protocol is designed to guarantee that all the tracking error signals in the considered multi-agent systems are ultimately bounded, under which the distributed tracking consensus is reached for all undirected connected communication graphs. Finally, two demonstrative examples are given to illustrate the validity of the designed protocols.