Fuzzy Neural Networks-Based Prescribed-Time Fault-Tolerant Cooperative Control of Second-Order Nonlinear Heterogeneous Multiagent Systems

This article investigates the prescribed-time fault-tolerant cooperative control problem for second-order nonlinear heterogeneous multiagent systems (MASs) subject to multiple actuator faults. A novel prescribed-time stability criterion, independent of time-varying gain functions, is established to facilitate convergence analysis. Fuzzy neural networks (FNNs) are employed to approximate the unknown heterogeneous nonlinear dynamics, and a unified fault-tolerant control framework is constructed to simultaneously address both actuator bias and loss-of-effectiveness (LOE) faults. Based on a nonsingular sliding-mode approach, a distributed control protocol is designed to achieve prescribed-time tracking and containment consensus. Finally, simulation results for both single-leader and multileader scenarios validate the theoretical findings.