Hardware Implementation of a Real-Time Neural Network Controller With a DSP and an FPGA for Nonlinear Systems
Seul Jung
Sung su Kim
Dept. of Mechatronics Eng., Chungnam Nat., Daejeon;
This paper appears in: Industrial Electronics, IEEE Transactions on
Publication Date: Feb. 2007
Volume: 54,
Issue: 1
On page(s): 265-271
ISSN: 0278-0046
INSPEC Accession Number: 9299050
Digital Object Identifier: 10.1109/TIE.2006.888791
Current Version Published: 2007-02-05
Abstract
In this paper, we implement the intelligent neural network controller hardware with a field programmable gate array (FPGA)-based general purpose chip and a digital signal processing (DSP) board to solve nonlinear system control problems. The designed intelligent control hardware can perform real-time control of the backpropagation learning algorithm of a neural network. The basic proportional-integral-derivative (PID) control algorithms are implemented in an FPGA chip and a neural network controller is implemented in a DSP board. By using a high capacity of an FPGA chip, the additional hardware such as an encoder counter and a pulsewidth modulation (PWM) generator is implemented in a single FPGA chip. As a result, the controller becomes cost effective. It was tested for controlling nonlinear systems such as a robot finger and an inverted pendulum on a moving cart to show performance of the controller
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