FPGA Implementation of the Multilayer Neural Network for the Speed Estimation of the Two-Mass Drive System
This paper presents a practical realization of a neural network (NN)-based estimator of the load machine speed for a drive system with elastic coupling, using a reconfigurable field-programmable gate array (FPGA). The system presented is unique because the multilayer NN is implemented in the FPGA placed inside the NI CompactRIO controller. The neural network used as a state estimator was trained with the Levenberg-Marquardt algorithm. Special algorithm for implementation of the multilayer neural networks in such hardware platform is presented, focused on the minimization of the used programmable blocks of the FPGA matrix. The algorithm code for the neural estimator implemented in C-RIO was realized using the LabVIEW software. The neural estimators are tested: offline (based on the measured testing database) and online (in the closed-loop control structure). These estimators are tested also for changeable inertia moment of the load machine of the drive system with elastic joint. Presented results of the experimental tests confirm that the multilayer NN, implemented in the FPGA with the use of the higher level programming language, ensures a high-quality state variable estimation of the two-mass drive system.
drive system field-programmable gate array (fpga) elastic joint state variable estimation neural networks (nns)