RNN Based Optimized Backstepping Controller of Fractional Order Nonlinear System

In this work, a Backstepping controller with Recurrent Neural Network is proposed for reducing the tracking error in the Fractional order nonlinear systems. Further, this paper focuses on the comparison of the proposed control strategy with existing similar approaches. The proposed controller is highly effective in minimizing tracking error when compared to the other controllers like Sliding mode-neuro-fuzzy, Dynamic surface-neuro fuzzy, Artificial Neural network, Event-triggered control, fuzzy-based reinforcement learning and echo state networks with adaptive backstepping controller. The proposed technique is implemented on Genesio- Tesi fractional order nonlinear system. The simulation results presented ensure stability and validate the ability of the controller for regulation and tracking.