Neural-network-based average tracking for nonlinear multi-agent systems with switching topologies
Shangjun Zhang, Jianting Lyu, Dai Gao, Xin Wang · 2020
This paper investigates the average tracking problem for a class of nonlinear multi-agent systems with switching topologies. The control design is developed for switching topologies without requiring the accurate mode with nonlinear dynamics. An adaptive neural-network-based control algorithm is proposed for a team of agents to track the average of multiple time-varying reference signals, of which the dynamics of each subsystem is assumed to be unknown and will be estimated by using adaptive neural network mechanism. Finally, a numerical simulation is given to illustrate the effectiveness of presented consensus protocol.