Cognitive Parameter Adaption in Regular Control Structures - Using Process Knowledge for Parameter Adaption

Martin Schmid, Simon Berger, Gunther Reinhart

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.

References

  1. Aström, K. J., Hägglund, T., 2004. Revisiting the ZieglerNichols step response method for PID control.
  2. Bayer, J., Osendorfer, C., Smagt, P., 2011: Learning Sequence Neighbourhood Metrics. Technical University Munich.
  3. Beuschel, M., 2000. Neuronale Netze zur Diagnose und Tilgung von Drehmomentschwingungen am Verbrennungsmotor, Technical University Munich.
  4. Eberhard, M., 2006. Optimisation of Filtration by Application of Data Mining Methods, Dissertation, Technical University Munich.
  5. Faridi, A.; Golian, A., 2011: Use of neural network models to estimate early egg production in broiler breeder hens through dietary nutrient intake. In: Poultry Science.
  6. Govindhasamy, J. J.; McLoone, S. F.; Irwin, George W.; French, J.; Doyle, R. P., 2005: Neural modelling, control and optimisation of an industrial grinding process. In: Control Engineering Practice 13.
  7. Guanyuz, Zhou (2012): Online incremental feature Learning with denoising autoencoders. International Conference on Artificial Intelligence and Statistic, 2012.
  8. Hafner, R. 2009: Dateneffiziente selbstlernende neuronale Regler. University Osnabrück.
  9. Hintz, Christian, 2003: Identifikation nichtlinearer mechatronischer Systeme mit strukturierten rekurrenten Netzen. Dissertation. Technical University München.
  10. Huang, S.H; Hong-Chao Z., 1994: Artificial neural networks in manufacturing: concepts, applications, and perspectives. IEEE Trans. Comp., Packag., Manufact. Technologies.
  11. Kipphan, H., 2002. Handbuch der Printmedien, Springer Verlag, Berlin.
  12. Moon, P.; et al. , 2008. Ink-jet printing process modeling using neural networks. International Electronics Manufacturing Technology Conference.
  13. Rangwala, S. Dornfeld, D. A., 1989. Learning and optimization of machining operations using computing abilities of neural networks, IEEE Transactions on Systems, Man, and Cybernetics.
  14. Ramesh. R., Mannan M.A., Poo A.N., 2002. Support vector machines model for Classification of Thermal Error in Machine Tools, in: The International Journal of Advanced Manufacturing Technology.
  15. Rajagopalan, Ramesh; Rajagopalan, Purnima, 1996. Applications of Neural Network in Manufacturing. International Conference on System Sciences.
  16. Suzuki, K, 2011. Artificial Neural Networks - Industrial and Control Engineering Applications.
  17. Wang, D., 1984. An investigation of the applicacability of Walker and Fetsko ink transfer equation, Rochester, New York.
  18. Rae, T.A; et al, 1996. The application of neural Networks to induction machine control. Proceedings of IEEE.
  19. Zell, A., 1994. Simulation neuronaler Netze, Oldenburg Verlag, München.
Download


Paper Citation


in Harvard Style

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


in Bibtex Style

@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},
}


in EndNote Style

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