Adaptive Neural Consensus for Fractional-Order Multi-Agent Systems With Faults and Delays

This article investigates the consensus control for a class of fractional-order (FO) nonlinear multi-agent systems (MASs). Severe sensor/actuator faults and time-varying delays are both considered in the FO MASs. The severe faults may cause unknown control directions in MASs. A new adaptive controller, which is composed of a distributed FO Nussbaum gain, an FO filter, and an auxiliary function, is presented to deal with the severe faults. To cope with the time-varying delays, two different methods are proposed based on barrier Lyapunov function and Lyapunov-Krasovskii function, respectively. Meanwhile, the radial basis function neural network (RBF NN) is applied to approximate the unknown nonlinear functions during the design procedures. This can result in a low-complexity controller. Finally, two simulation examples are used to verify the validity of the proposed schemes.

Automated summary

This article investigates consensus control for a class of fractional-order nonlinear multi-agent systems (MASs) considering severe sensor/actuator faults and time-varying delays. A new adaptive controller, composed of distributed FO Nussbaum gain, FO filter, and auxiliary function, is proposed to handle severe faults. Two different methods based on barrier Lyapunov function and Lyapunov-Krasovskii function are proposed to deal with time-varying delays. Meanwhile, the radial basis function neural network (RBF NN) is applied to approximate unknown nonlinear functions, resulting in a low-complexity controller. Two simulation examples are used to verify the validity of the proposed schemes.