Adaptive single-neuron controller design for nonlinear process control

Abstract

In this paper, a new adaptive single-neuron (ASN) controller is proposed based on the just-in-time learning (JITL) technology for nonlinear process control. To mimic the traditional PID controller, a single neuron is employed in the proposed controller design strategy. Incorporated with the neural network's learning ability, the proposed controller can control the process adaptively through the updating of its parameters by the adaptive learning algorithm developed and the information provided from the JITL. Compared with the neural network based PID controller designs previously developed, ASN controller is more amenable to on-line implementation. Simulation results are presented to illustrate the proposed method and a comparison with its conventional counterparts is made. Copyright © 2008 The Society of Chemical Engineers, Japan.