Neural‐network‐embedded distributed average tracking of agents with matching unknown nonlinearities

Abstract

AbstractThis paper studies a distributed average tracking problem for a class of networked agents subject to heterogeneous unknown nonlinearities. The object is to design distributed protocols so as to drive these dynamic agents cooperatively tracking the average of multiple unknown signals. First, an initialize‐free robust algorithm is designed for each agent incorporating a local filter, a neural network (NN) compensator, and state‐dependent coupling gains with its neighbors. Here the filter is crucial for seeking the average of multiple references signals and is necessary due to the existence of uncertainties in the agents' dynamics. Then, by using adaption schemes, the algorithm is extended to a dynamic version releasing the requirement of certain global information such as the eigenvalues of the network Laplacian and the NN approximation errors. Both algorithms are rigorously proved to guarantee asymptotical average tracking with the help of well‐designed Lyapunov candidates. Finally, two illustrative examples are provided to validate the theoretical results.