Prescribed-time fault-tolerant consensus for uncertain nonlinear multi-agent systems

Abstract

Achieving consensus within a user-defined time frame for uncertain nonlinear systems is both crucial and challenging. To tackle this issue, we propose an adaptive consensus protocol that utilizes a radial basis function neural network to handle unknown nonlinearities and actuator faults. Unlike traditional finite-time or fixed-time consensus methods, our approach employs continuous, time-varying feedback to guarantee convergence within the desired time. The proposed strategy ensures that all closed-loop signals of the system remain bounded, achieving consensus within the prescribed time. The effectiveness of the proposed control strategy is demonstrated through a simulation example of phase synchronization in a power system.