Recurrent neural network-based technique for synchronization of fractional-order systems subject to control input limitations and faults

Abstract

Despite the existence of promising methods for controlling complex systems, there is still a need for further advancement in controlling and synchronizing fractional systems. This is even more monumental when it comes to practical applications with faults and physical constraints present in their control actuators. To address this issue, the current study proposes a new control technique that utilizes a recurrent neural network-based finite-time super-twisting algorithm for fractional-order systems. The proposed controller is enhanced with an intelligent observer to account for faults and limitations that may be present in the control actuator of the fractional-order systems. The proposed method allows the system to be regulated even in the presence of control input constraints and faults. Moreover, the proposed technique guarantees that the closed-loop system will converge in a finite amount of time. The control design is explained in detail, and its finite-time stability is proven. To evaluate the performance of the controller, we applied it to two different fractional-order systems that were subject to control input limitations and faults. The outcomes of the proposed approach were then compared with those obtained using a state-of-the-art technique for fractional-order systems to further validate the effectiveness of our proposed approach.