An intelligent approach to integration and control of textile processes
This paper introduces a methodology to integrate and control effectively major plant processes with strong couplings between them. The proposed integration philosophy consists of causeeffect relationships and decides upon control setpoints for the individual processes by optimizing a global objective function which aims at improving process yield. A neuro-fuzzy model and a fuzzy objective function are employed to address the integration and control tasks. Such models and objective functions are defined and developed using experimental data or an operator's experience. The objective is to maximize productivity and at the same time, reduce defects in each of the subsequent operations. A textile plant is considered as a testbed and three major processes  warping, slashing and weaving  are employed to illustrate the feasibility of the approach. The supervisory level of the control architecture is intended to continuously improve the control setpoints depending upon feedback information from the weave room, slasher operator, and warping data.
integration polynomial fuzzy neural networks fuzzy logic control causeeffect relation genetic algorithms hybrid genetic optimization