Traditionally manufacturing systems are controlled using Programmable Logic Controllers, which often require human intervention for system reprogramming on the arrival of a new product. In order to reduce all related costs associated with the human intervention, new systematic and automated system engineering approaches are needed.This thesis introduces a knowledge-based approach to the modeling and control of manufacturing systems aiming to capture the engineering knowledge. In order to demonstrate the applicability of the methodology, a software tool was implemented and applied to the case study, a pallet-based lifter used in electronics assembly, taken from the domain of factory automation. Early experiences show that the introduced approach can be used to capture knowledge pertaining to manufacturing equipments, processes and products. The approach could be considered as a potential solution for the implementation of reconfigurable control systems.
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