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Adaptive Neural Consensus Control for Nonlinear Strict-Feedback Multiagent Systems With Switching Directed Topology

This paper focuses on solving the adaptive consensus tracking problem of uncertain nonlinear strict-feedback multiagent systems with switching directed topology. From a viewpoint of switched system, the neighborhood synchronization error can be regarded as a nonlinear switching system and the switching signal is caused by the change of the topology, then an appropriate common Lyapunov function is constructed for the whole multiagent system with switching topology. By using the radial basis function neural networks (RBFNNs), the unknown nonlinear functions are compensated during the back-stepping design procedure. Especially, instead of the common first-order filter in the conventional dynamic surface control (CDSC) technique, a novel nonlinear observer is presented to improve the control performance. A new common adaptive neural consensus control protocol is proposed for such systems based on the structure property of RBFNNs and the common Lyapunov function method. The developed control scheme guarantees that the consensus tracking errors between all followers’ outputs and the output of leader can converge to a small neighborhood of the origin in the presence of switching directed communication topology. Finally, two illustrative examples are provided to show the effectiveness of the proposed consensus control methodology.