Development and performance of a neural-network predictive controller

Neural-network techniques are investigated in an application to the identification and subsequent on-line control of a process exhibiting non-linearities and typical disturbances. The design and development of a neural-network process model from measured data is described, and practical aspects of the identification procedure are discussed. Results demonstrate that the developed neural-network representation of the process dynamics is sufficiently accurate to be used independently from the process, emulating the process response from only process input information. Accurate long-range predictions from the neural-network model are mainly due to the use of a novel spread encoding technique for representing data in the network. Implementation of a predictive control strategy incorporating the identified neural-network model is described, and on-line results illustrate the improvements in control performance that can be achieved when compared to conventional proportional-plus-integral control.