PREDICTIVE CONTROL OF QUALITY IN A BATCH MANUFACTURING PROCESS USING ARTIFICIAL NEURAL-NETWORK MODELS

Artificial neural networks (ANN) can be used to generate models of batch processes relating product quality to process input variables and processing conditions used. Such models can be used in a noinlinear model predictive scheme to control product quality. Intermediate measurements taken while batch processing is in progress can provide feedback correction to compensate for modeling errors and unmeasured disturbances. This paper presents an architecture for a shrinking horizon model predictive control of batch processes using ANN models, and its application to a simulated autoclave curing process for composite manufacturing. In addition, the concept of incremental learning using ANNs to provide on-line adaptation to changing processing conditions is also explored in this paper.