Adaptive Consensus Tracking Control for Nonlinear Multi-Agent Systems with Input Saturation and Dead-Zone
Shubo Li, Yingnan Pan, Hongjing Liang · 2019
This paper investigates the adaptive consensus tracking control problem for multi-agent systems (MASs) with input saturation and dead-zone. Radial basis function neural networks (RBF NNs) are utilized to estimate the unknown nonlinear functions. An adaptive consensus tracking control scheme is proposed by the backstepping technique and Lyapunov stability theory. The proposed controller guarantees that all the closed-loop system signals are bounded and the outputs of followers can track the leader. Finally, numerical example demonstrates the effectiveness of the obtained results.