Hardware Implementation of a RBF Neural Network Controller with a DSP 2812 and an FPGA for Controlling Nonlinear Systems
Geun-Hyung Lee, Sung‐Su Kim, Seul Jung · 2008
This paper presents the hardware implementation of the neural network controller for controlling nonlinear systems. The neural network controller is implemented on the digital signal processing (DSP) chip and a field programmable gate array (FPGA) chip. The DSP 2812 controller board has been developed for controlling motors. Combining the DSP and the FPGA yields the neural network controller. The reference compensation technique (RCT) as a neural network learning algorithm is implemented. Experimental studies of balancing the angle and controlling the cart of the inverted pendulum system have been conducted to confirm the performance of the hardware implementation of the neural controller.