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Trends, Challenges and Research Opportunities

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Part of the book series: Advances in Industrial Control ((AIC))

Abstract

Several research topics remain to be fully explored before we are able to say that the application of GP models for control is a mature technology, ready to use in everyday engineering practice. The trends, challenges and research opportunities related to GP model-based control-systems design are indicated in this chapter.

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References

  1. Ljung, L.: Perspectives on system identification. In: Proceedings of IFAC 17th World Congress 2008, pp. 1–6. Seoul (2008)

    Google Scholar 

  2. Frigola, R., Rasmussen, C.E.: Integrated pre-processing for Bayesian nonlinear system identification with Gaussian processes. In: 52nd IEEE Conference on Decision and Control (CDC) (2013)

    Google Scholar 

  3. McHutchon, A., Rasmussen, C.E.: In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K. (eds.) Advances in Neural Information Processing Systems. Gaussian process training with input noise, vol. 24, pp. 1341–1349 (2011)

    Google Scholar 

  4. Turner, R., Deisenroth, M.P., Rasmussen, C.E.: State-space inference and learning with Gaussian processes. In: Proceedings of 13th International Conference on Artificial Intelligence and Statistics, vol. 9, pp. 868–875. Sardinia (2010)

    Google Scholar 

  5. Frigola, R., Lindsten, F., Schön, T.B., Rasmussen, C.E.: Bayesian inference and learning in Gaussian process state-space models with particle MCMC. In: Bottou, L., Burges, C., Ghahramani, Z., Welling, M., Weinberger, K. (eds.) Advances in Neural Information Processing Systems 26, pp. 3156–3164 (2013)

    Google Scholar 

  6. Saatçi, Y., Turner, R., Rasmussen, C.E.: Gaussian process change point models. In: Proceedings of the 27th Annual International Conference on Machine Learning, pp. 927–934 (2010)

    Google Scholar 

  7. The Gaussian processes web site. http://www.gaussianprocess.org/#code

  8. Rasmussen, C.E., Nickisch, H.: Gaussian Processes for Machine Learning (GPML) toolbox. J. Mach. Learn. Res. 11, 3011–3015 (2010)

    MATH  MathSciNet  Google Scholar 

  9. Vanhatalo, J., Riihimäki, J., Hartikainen, J., Jylänki, P., Tolvanen, V., Vehtari, A.: GPstuff: Bayesian modeling with Gaussian processes. J. Mach. Learn. Res. 14, 1175–1179 (2013)

    MATH  MathSciNet  Google Scholar 

  10. Venkatasubramanian, V., Rengaswamy, R., Yin, K., Kavuri, S.N.: A review of process fault detection and diagnosis: Part I: Quantitative model-based methods. Comput. Chem. Eng. 27(3), 293–311 (2003)

    Article  Google Scholar 

  11. Kocijan, J., Přikryl, J.: Soft sensor for faulty measurements detection and reconstruction in urban traffic. In: Proceedings 15th IEEE Mediterranean Electromechanical Conference (MELECON), pp. 172–177. Valletta (2010)

    Google Scholar 

  12. Osborne, M.A., Garnett, R., Roberts, S.J.: Active data selection for sensor networks with faults and changepoints. In: IEEE International Conference on Advanced Information Networking and Applications (2010)

    Google Scholar 

  13. Osborne, M.A., Garnett, R., Swersky, K., de Freitas, N.: Prediction and fault detection of environmental signals with uncharacterised faults. In: 26th AAAI Conference on Artificial Intelligence (AAAI-12). Toronto (2012)

    Google Scholar 

  14. Kullaa J (2013) Detection, identification, and quantification of sensor fault in a sensor network. Mechanical Systems and Signal Processing 40(1):208–221. doi:10.1016/j.ymssp.2013.05.007

    Google Scholar 

  15. Butler, S., Ringwood, J., O’Connor., F.: Exploiting SCADA system data for wind turbine performance monitoring. In: Conference on Control and Fault-Tolerant Systems (SysTol) October 9–11, Nice (2013)

    Google Scholar 

  16. Juričić, a., Ettler, P., Kocijan, J.: Fault detection based on Gaussian process models: An application to the rolling mill. In: ICINCO 2011—Proceedings of the 8th International Conference on Informatics in Control, Automation and Robotics, vol. 1, pp. 437–440 (2011)

    Google Scholar 

  17. Juričić, D., Kocijan, J.: Fault detection based on Gaussian process model. In: Troch, I., Breitenecker, F. (eds.) Proceedings of the 5th Vienna Symposium on Mathematical Modeling (MathMod). Vienna (2006)

    Google Scholar 

  18. Eciolaza, L., Alkarouri, M., Lawrence, N.D., Kadirkamanathan, V., Fleming, P.: Gaussian process latent variable models for fault detection. In: Proceedings of the 2007 IEEE Symposium on Computational Intelligence and Data Mining (CIDM 2007), pp. 287–292. Honolulu, HI (2007)

    Google Scholar 

  19. Serradilla, J., Shi, J.Q., Morris, J.A.: Fault detection based on Gaussian process latent variable models. Chem. Intel. Lab. Sys. 109(1), 9–21 (2011)

    Article  Google Scholar 

  20. Lawrence, N.: Gaussian process latent variable models for visualization of high dimensional data. In: Thrun, S., Saul, L., Schlkopf, B. (eds.) Advances in Neural Information Processing Systems, pp. 329–336. The MIT Press, Cambridge, MA (2004)

    Google Scholar 

  21. Vachtsevanos, G., Lewis, F.L., Roemer, M., Hess, A., Wu, B.: Intelligent Fault Diagnosis and Prognosis for Engineering Systems. Wiley, Hoboken (2006)

    Book  Google Scholar 

  22. Kocijan, J., Tanko, V.: Prognosis of gear health using Gaussian process model. In: Proceedings IEEE Eurocon 2011 International Conference Computer as a tool. Lisbon (2011)

    Google Scholar 

  23. Liu, D., Pang, J., Zhou, J., Peng, Y., Pecht, M.: Prognostics for state of health estimation of lithium-ion batteries based on combination Gaussian process functional regression. Microelectron. Reliab. 53(6), 832–839 (2013)

    Article  Google Scholar 

  24. Mohanty, S., Das, S., Chattopadhyay, A., Peralta, P.: Gaussian process time series model for life prognosis of metallic structures. J. Intel. Mater. Syst. Struct. 20(8), 887–896 (2009)

    Article  Google Scholar 

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Correspondence to Juš Kocijan .

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Kocijan, J. (2016). Trends, Challenges and Research Opportunities. In: Modelling and Control of Dynamic Systems Using Gaussian Process Models. Advances in Industrial Control. Springer, Cham. https://doi.org/10.1007/978-3-319-21021-6_5

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  • DOI: https://doi.org/10.1007/978-3-319-21021-6_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-21020-9

  • Online ISBN: 978-3-319-21021-6

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