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

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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

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