Recurrent Neural Network Based Backstepping Controller for Genesio-Tesi Chaotic System
A. Narmada, Anuj Jain, Manoj Kumar Shukla · 2024
Fractional calculus has drawn a lot of interest lately for its application in modelling and managing real-world systems. Fractional order (FO) nonlinear strict feedback system suffers from tracking issues and unknown parameters. This nonlinear system includes internal parameter uncertainty and external disruptions. Therefore, this paper proposed a novel controller to enhance the stability in nonlinear system under actuator faults. Recurrent neural network (RNN) based backstepping controller is proposed in this work for FO nonlinear system. Backstepping controller are mostly used with nonlinear systems. The instability is approximated using adaptively coupled RNN in the controller. RNN estimates the corresponding nonlinear component in the control expression to reduce the error in the system. This proposed method is implemented in gensio-tesi chaotic system. Verification of proposed scheme is carried out in MATLAB tool. Results of proposed method are analyzed with existing method to show performance of this system.