Finite time adaptive fault-tolerant controller based on neural networks for switched stochastic nonlinear systems with actuator failures and disturbance

This article investigates the novel finite time adaptive neural fault-tolerant controller (FTC) for strict-feedback switched stochastic systems under arbitrary switching signals and takes into actuator failures including loss of effectiveness faults and bias faults consideration concurrently. Neural networks are utilized to approximate the unknown external disturbance and internal dynamics. On the basis of Ito differential equation and backstepping technique, an adaptive neural finite time FTC method is put forward. It is attested that the closed-loop systems are semiglobal practical finite time stable in probability and the tracking effects are great. Finally, to further demonstrate the high efficiency of proposed control method, two simulation examples are given.