Neuro-adaptive consensus strategy for a class of nonlinear time-delay multi-agent systems with an unmeasurable high-dimensional leader - Yue, Dongdong; Cao, Jinde; Li, Qi; Shi, Xinli (IET control theory & applications, 2019)
Distributed cooperative consensus tracking problem for a class of uncertain multi-agent systems with a high-dimensional leader under a directed communication topology is concerned. Compared with related works, the dynamics of the leader agent is allowed to be different from those of the followers and may not be measured. Meanwhile, the dynamics of each follower is subject to un-modelled dynamics, unknown time-varying delays, as well as external disturbances, which makes the model more suitable in various practical applications. Based on the $M$M-matrix and Lyapunov–Krasovskii functional method, a distributed robust radial basis function neural network controller as well as a local observer is designed for each follower so as to guarantee the ultimate boundness of the tracking errors to the leader's output signals. By appropriately cutting down the neural network parameters to be updated online, the computational burden can be greatly reduced. The effectiveness of the proposed consensus approach is testified via a numerical example.