Hardware Implementation of a Real-Time Neural Network Controller With a DSP and an FPGA for Nonlinear Systems
Seul Jung; Sung su Kim
Industrial Electronics, IEEE Transactions on
Volume 54, Issue 1, Feb. 2007 Page(s):265 - 271
Digital Object Identifier 10.1109/TIE.2006.888791
Summary: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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