Adaptive Inverse Neural Network Based DC Motor Speed and Position Control Using FPGA

Abbas H. Issa; Aula N. Abd

doi:10.24237/djes.2018.11311

Authors

Abbas H. Issa
30050@uotechnology.edu.iq
Department of Electrical Engineering, University of Technology, Iraq
Aula N. Abd Department of Electrical Engineering, University of Technology, Iraq

Keywords:

DC motor, speed control, position control, PID, ANN, Inverse Neural Controller, FPGA

Abstract

In this research two types of controllers are designed in order to control the speed and position of DC motor. The first one is a conventional PID controller and the other is an intelligent Neural Network (NN) controller that generate a control signal DC motor. Due to nonlinear parameters and movable laborers such saturation and change in load a conventional PID controller is not efficient in such application; therefore neural controller is proposed in order to decreasing the effect of these parameter and improve system performance. The proposed intelligent NN controller is adaptive inverse neural network controller designed and implemented on Field Programmable Gate Array (FPGA) board. This NN is trained by Levenberg-Marquardt back propagation algorithm. After implementation on FPGA, the response appear completely the same as simulation response before implementation that mean the controller based on FPGA is very nigh to software designed controller. The controllers designed by both m-file and Simulink in MATLAB R2012a version 7.14.0.

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