By Topic

Hardware implementation of a real time neural network controller with a DSP and an FPGA

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)
Sung Su Kim ; Dept. of Mechatronics Eng., Chungnam Nat. Univ., Daejeon, South Korea ; Seul Jung

In this paper, we implement the intelligent controller hardware such as a neural network controller with an FPGA based general purpose controller and a DSP board to solve nonlinear control problems. The designed control hardware can perform a real time control of the backpropagation learning algorithm of a neural network. The basic PID control algorithms are implemented in an FPGA chip and a neural network controller is implemented in a DSP board. By using high capacity of an FPGA, the additional hardware such as an encoder counter and a PWM generator can be implemented in a single FPGA device. As a result, the controller is very cost effective. In order to show the performance of the controller, it was tested for controlling nonlinear systems such as an inverted pendulum.

Published in:

Robotics and Automation, 2004. Proceedings. ICRA '04. 2004 IEEE International Conference on  (Volume:5 )

Date of Conference:

26 April-1 May 2004