By Topic

FPGA Implementation of the Multilayer Neural Network for the Speed Estimation of the Two-Mass Drive System

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Orlowska-Kowalska, T. ; Inst. of Electr. Machines, Drives & Meas., Wroclaw Univ. of Technol., Wroclaw, Poland ; Kaminski, M.

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.

Published in:

Industrial Informatics, IEEE Transactions on  (Volume:7 ,  Issue: 3 )