Abstract

FPGAs are a hardware solution for applications that require low power usage and real time execution. This work focuses on motor control. We show the design and FPGA implementation of a motor controller based on a small Neural Network (NN) as alternative to a traditional Proportional Integral (PI) controller that was used as reference. Performance metrics are defined from a simulation of the target motor. We investigate different NNs, training, code generation methods and numerical precisions. The best-performing controller is a multilayer NN trained with Reinforced Learning. The controller is implemented and runs on an Intel MAX 10 FPGA. The methodology described in this work is easily applicable to more complex designs when FPGAs become the best implementation platform.

Full Text

Published Version

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.