A new rbf neural network-based fault-tolerant active control for fractional time-delayed systems

Recently, intelligent control techniques have received considerable attention. In most studies, the systems’ model is assumed to be without any delay, and the effects of faults and failure in actuators are ignored. However, in real practice, sensor malfunctioning, mounting limitation, and defects in actuators bring about faults, failure, delay, and disturbances. Consequently, applying controllers that do not consider these problems could significantly deteriorate controllers’ perfor-mance. In order to address this issue, in the current paper, we propose a new neural network-based fault-tolerant active control for fractional time-delayed systems. The neural network estimator is integrated with active control to compensate for all uncertainties and disturbances. The suggested method’s stability is achieved based on the concept of active control and the Lyapunov stability theorem. Then, a fractional-order memristor system is investigated, and some characteristics of this chaotic system are studied. Lastly, by applying the proposed control scheme, synchronization results of the fractional time-delayed memristor system in the presence of faults and uncertainties are studied. The simulation results suggest the effectiveness of the proposed control technique for uncertain time-delayed nonlinear systems.