Disturbance Detection, Recover and Prediction in Holonic Manufacturing Control

Abstract

Disturbance handling is a crucial issue in the development of intelligent and reconfigurable manufacturing control systems, supporting the fast and promptly response to the occurrence of unexpected disturbances. In holonic manufacturing control systems, the disturbance handling functions are distributed by the several autonomous control units. In this paper, a predictive disturbance handling approach is presented, transforming the traditional "fail and recover" practices into "predict and prevent" practices, allowing improving the control system performance