As a different way to approximate and control the dynamics involved in a 2 DOF Robot, the novel control architecture presented in [17] employs an adaptative neural network using proportional derivative control comparison plus a feedforward fuzzy controller, where the NN is pre-trained in simulation several times to obtain a suitable values of weights to avoid overshoots in the torque being applied to every joint. Another method of controlling a non-linear system is the neuro-fuzzy controller for the position control arm used in [18], where a five layer neural network is used to adjust the input and output parameters of the membership function in a fuzzy logic controller, concluding that this proposed control is better than a proportional-integral-derivative (PID) controller for a robot trajectory. Finally, in [5], a real-time implementation of a recurrent wavelet first-order neural network is presented. In the mentioned paper is proposed to achieve better identification of the dynamic behavior where a 2 DOF robot manipulator is used for designing a centralized neural integrator back stepping control scheme. In this paper, we propose a control scheme based on a decentralized recurrent high-order neural network (RHONN) in order to identify the dynamics of a 2 DOF system in discrete time, coupled with the SMC technique. The RHONN is trained online using the modified EKF reported in [12]. This training algorithm ensures fast learning convergence of the identification error. The proposed control system includes the RHONN identification of the plant and the application of the SMC control technique, ensuring the stability and robustness of the system. The validation of the system is carried out in a hardware-in-loop architecture based on Xilinx System Generator (XSG) software [19], which enables the use of a Matlab/Simulink environment to create and verify hardware designs, using a Virtex 7 field programmable gate array (FPGA). The hardware-in-loop (HIL) simulation is used to test the controller system design to verify the response of the system in real time, due to virtual stimulus without the burden of sacrificing scarce programmable resources [20]. The main contributions can be highlighted as follows. * The control system design and implementation for a 2 DOF manipulator robot using a RHONN in a hardware-in-loop architecture. * Is presented a methodology for identification and tracking of the 2 DOF manipulator robot. * A Real Time implementation on an FPGA of the proposed control system based in a RHONN with an EKF algorithm with an SMC. * Inside the EKF the FPGA computes the associated state and measurement noise covariance matrices composed by the coupled variance between the plant states. * The results are obtained with a hardware-in-loop architecture and the experiments and the results show the high performance of the system. Section 2 presents the discrete-time 2 DOF robot manipulator model used and the rest of this paper is mainly divided into three parts. First, in Section 3, we present the control system architecture, and explain the neural identification method based on the RHONN, the extended Kalman filtering training algorithm and the discrete time sliding mode controller. Secondly, the design and implementation in real time based on hardware-in-loop architecture are presented in Section 4. Finally, in Section 5 we present the experimental tests with real-time results to validate the performance of the system in the identification process and the tracking control.