Adaptive FTC Consensus Control for Nonlinear Multi-agent Systems Based on Neural Networks
Xianfeng Zhao, Xiaozheng Jin · 2019
This paper investigates the adaptive fault tolerant consensus control (FTC) problem based on neural networks for nonlinear multi-agent systems with actuator faults. Adaptive technique based on RBF neural network is proposed to achieve the asymptotic consensus results of the multi-agent system. Respectively, all signals of the resulting closed loop multi-agent system are bounded by using Lyapunov stability theorem and adaptive distributed robust consensus control scheme. Finally, a simulation example is demonstrated to show the effectiveness of the consensus control strategies.