Implementation of a neural network controller and estimator using a digital signal processing chip

Abstract

In this paper, artificial neural networks with error backpropagation are considered for the control of linear and nonlinear dynamics. A neural network estimator and controller is constructed and trained off-line to learn the dynamic behavior and satisfactory control. The neural network estimator generates estimates of the plant output and also provides the plant Jacobian for the neural network controller. For real-time control purpose, the neural network estimator and controller is implemented in assembly language using a Motorola DSP56001 digital signal processing chip. The time to convergence can be shortened by utilizing the computational speed of the chip. The nonlinear activation function of the neural network is approximated and stored as a look-up table. Simulation results between the DSP and C language versions agree well without any noticeable degradations.