FPGA realization of the neural speed estimator for the drive system with elastic coupling

In this paper implementation of neural network based estimator of the load machine speed for two-mass drive system on a reconfigurable field-programmable gate array (FPGA) is presented. Due to such implementation, the developed neural estimator performs the parallel signal processing and thus the realization time is much shorter than using classical DSP. The FPGA based neural network structure has been carried out in the controller NI CompactRIO. The neural estimator design process, including input vector preprocessing, training and testing procedure is presented. The main part of research is concentrated on implementation of neural networks estimators with required internal structure in FPGA matrices. Neural estimators are tested for changeable moment of inertia of the load machine in the drive system with elastic joint. High accuracy of reconstructed signals is obtained without the necessity of the electrical drive system parameters identification.