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Authors: Martin Schmid 1 ; Simon Berger 1 and Gunther Reinhart 2

Affiliations: 1 Project Group RMV of Fraunhofer IWU, Germany ; 2 Technical University Munich, Germany

ISBN: 978-989-8565-70-9

Keyword(s): Modern Control Systems, Adaption, Neural Network, Machine Learning.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence and Decision Support Systems ; Distributed Control Systems ; Engineering Applications ; Enterprise Information Systems ; Informatics in Control, Automation and Robotics ; Intelligent Control Systems and Optimization ; Knowledge-Based Systems Applications ; Machine Learning in Control Applications ; Neural Networks Based Control Systems ; Optimization Algorithms ; Robotics and Automation ; Signal Processing, Sensors, Systems Modeling and Control

Abstract: The colour control system of an offset printing machine is one example, where modern information processing technologies allow an improved process control and higher resource efficiency. It is not possible to measure the printing quality during production start. So no regular closed loop control can be used. For better system behaviour a simulation model is integrated to calculate the printing quality at any time. To get an optimal process performance, a high simulation quality must be ensured, which includes a compensation of process simulation inaccuracies as well as variable influences. Therefore a cognitive system is installed, which measures the most important influences like the used paper and many other process parameters. After each production the right model parameters will be calculated by identification algorithms. So a data set with influences and parameters is available. For the next production run the best-fitting parameters for the simulation model can be calculated by a Neural Network. Additionally wear and deposits, which change the machine’s performance, can be compensated. The simulation accuracy and the process control quality rises, which enables a faster run-up. Savings of paper, ink, energy and time allow an economic application of this control concept. (More)

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Paper citation in several formats:
Schmid, M.; Berger, S. and Reinhart, G. (2013). Cognitive Parameter Adaption in Regular Control Structures - Using Process Knowledge for Parameter Adaption.In Proceedings of the 10th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-8565-70-9, pages 131-138. DOI: 10.5220/0004427701310138

@conference{icinco13,
author={Martin Schmid. and Simon Berger. and Gunther Reinhart.},
title={Cognitive Parameter Adaption in Regular Control Structures - Using Process Knowledge for Parameter Adaption},
booktitle={Proceedings of the 10th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2013},
pages={131-138},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004427701310138},
isbn={978-989-8565-70-9},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - Cognitive Parameter Adaption in Regular Control Structures - Using Process Knowledge for Parameter Adaption
SN - 978-989-8565-70-9
AU - Schmid, M.
AU - Berger, S.
AU - Reinhart, G.
PY - 2013
SP - 131
EP - 138
DO - 10.5220/0004427701310138

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