Recurrent neural identification on Xilinx system generator using V7 FPGA for a 2DOF robot manipulator

Abstract

This paper describes an identification process for a class of discrete-time nonlinear systems, which includes the Xilinx system generator software and the process is implemented in a Virtex 7 (V7) field programmable gate array (FPGA). This procedure consists of programming a discrete-time nonlinear plant where the dynamics of this plant is reproduced by a discrete-time recurrent high order neural network (RHONN). The neural network is trained on-line with the extended Kalman filter algorithm where the associated state and measurement noises covariance matrices are composed by the coupled variance between the plant states. Additionally, a sliding window-based method for dynamical modeling of nonstationary systems is presented in order to improve the neural identification process. This identification process is implemented on a Virtex 7 (V7) FPGA using Xilinx system generator software where are programed in this FPGA: the discrete-time dynamics of the two degrees of freedom (2DOF) robot manipulator, the RHONN, the extended Kalman filter (EKF) training algorithm and the sliding windowbased method. The obtained results from the FPGA are compared with the results obtained from Matlab/SImulink in order to validate the identification process for the present proposal.