Simulated hardware design of artificial neural networks for adaptive plant control

Abstract

In this paper, an artificial neural network (ANN) hardware circuit design for implementing online plant parameter identification and plant control is presented. The parallel structure of the ANN hardware is typical of the feedforward network with a real-time back-propagation training algorithm. The circuit is designed for implementing energy function minimization and the gradient descent algorithm. Different schemes of the hardware design are discussed for realizing adaptive control functions. Simulated results show that the proposed ANN circuit design has fulfilled the performance objective as required.