In this paper, an adaptive Super Twisting Algorithm (STA) based Second Order Sliding Mode Controller (SOSMC) design using a novel Gated Recurrent Unit (GRU) blended Redial Basis Function Neural Network (RBFNN) structure is proposed. In this GRU blended RBFNN structure, the GRU is placed in 1st hidden layer and the RBF is placed in the 2nd hidden layer to enhance the estimation of unknown function f (x) of the nonlinear dynamical system. The estimated function is used to design an adaptive STA of the SOSMC with adaptive laws for updating STA gains. The closed loop asymptotic stability and finite time convergence of the system is also ensured using the Lyapunov theory. Finally, the controller is implemented on two link Robotic Arm with serial flexible joints to show the performance and efficacy. The experimental results exhibit an excellent tracking performance and robustness of the controller.