Finite-time combination synchronization of neural networks with uncertainties via adaptive fault-tolerant control

In this study, problems of finite-time combination synchronization of neural networks with uncertainties are investigated using adaptive fault-tolerant control methods. When the neural networks system parameters and uncertainties are unknown, adaptive controllers are designed. The fault tolerance strategy ensures that when the system experiences stuck faults and actuator partial failures, the error dynamic system can still converge to the equilibrium point within a finite time. Finally, the correctness of the theorem and the effectiveness of the fault tolerant controller were verified through actual numerical simulations.