Abstract
Current manufacturing processes are characterized by their high complexity and require an increased control effort. Operating them effectively and efficiently is crucial and knowledge integration methods can make a valuable contribution to this. Presented here is a generic model predictive system that enables the integration of different sources of knowledge. In addition, the system is adaptive and allows for a self-adaptation to changing operating conditions and a self-optimization. The implementation of an inferential control mechanism finally ensures continuous process control in the absence of primary measurements.
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Berlik, S. (2011). A Generic Knowledge Integration Approach for Complex Process Control. In: Fathi, M., Holland, A., Ansari, F., Weber, C. (eds) Integrated Systems, Design and Technology 2010. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17384-4_23
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DOI: https://doi.org/10.1007/978-3-642-17384-4_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17383-7
Online ISBN: 978-3-642-17384-4
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