Skip to main content
SpringerLink
Log in

Reduction of Relative Degree by Optimal Control and Sensor Placement

  • Chapter
  • First Online:
Network-Based Analysis of Dynamical Systems

Part of the book series: SpringerBriefs in Computer Science ((BRIEFSCOMPUTER))

  • 365 Accesses

Abstract

However, the resulting proportions of driver and sensor nodes are particularly small when compared to the size of the system, and although structural controllability and observability is ensured, the system demands additional drivers and sensors to provide the small relative degree needed for fast and robust process monitoring and control. In this chapter, a centrality measures-based, two set covering-based and two clustering and simulated annealing-based methods are proposed to assign additional drivers and sensors to the dynamical systems.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Aguirre-Salas, L., Begovich, O., Ramirez-trevino, A.: Sensor assignment for observability in interpreted petri nets. IFAC Proc. Vol. 37(18), 441–446 (2004)

    Article  Google Scholar 

  2. Bagajewicz, M.J.: Design and retrofit of sensor networks in process plants. AIChE J. 43(9), 2300–2306 (1997)

    Google Scholar 

  3. Berger-Wolf, T.Y., Hart, W.E., Saia, J.: Discrete sensor placement problems in distribution networks. Math. Comput. Model. 42(13), 1385–1396 (2005)

    Google Scholar 

  4. Blanloeuil, P., Nurhazli, N.A.E., Veidt, M.: Particle swarm optimization for optimal sensor placement in ultrasonic shm systems. In: Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, and Civil Infrastructure 2016, vol. 9804, p. 98040E. International Society for Optics and Photonics (2016)

    Google Scholar 

  5. Boukhobza, T., Hamelin, F.: State and input observability recovering by additional sensor implementation: a graph-theoretic approach. Automatica 45(7), 1737–1742 (2009)

    Article  MathSciNet  Google Scholar 

  6. Carballido, J.A., Ponzoni, I., Brignole, N.B.: Cgd-ga: a graph-based genetic algorithm for sensor network design. Inf. Sci. 177(22), 5091–5102 (2007)

    Google Scholar 

  7. Chahlaoui, Y., Van Dooren, P.: A collection of benchmark examples for model reduction of linear time invariant dynamical systems (2002)

    Google Scholar 

  8. Chiu, P.L., Lin, F.Y.S.: A simulated annealing algorithm to support the sensor placement for target location. In: 2004 Canadian Conference on Electrical and Computer Engineering, vol. 2, pp. 867–870. IEEE (2004)

    Google Scholar 

  9. Chmielewski, D.J., Palmer, T., Manousiouthakis, V.: On the theory of optimal sensor placement. AIChE J. 48(5), 1001–1012 (2002)

    Google Scholar 

  10. Shu-Chuan, C., Roddick, J.F., Pan, J-S.: A comparative study and extension to K-medoids algorithms, Contemporary Development Company (2001)

    Google Scholar 

  11. Daoutidis, P., Kravaris, C.: Structural evaluation of control configurations for multivariable nonlinear processes. Chem. Eng. Sci. 47(5), 1091–1107 (1992)

    Article  Google Scholar 

  12. Düştegör, D., Frisk, E., Cocquempot, V., Krysander, M., Staroswiecki, M.: Structural analysis of fault isolability in the damadics benchmark. Control Eng. Pract. 14(6), 597–608 (2006)

    Article  Google Scholar 

  13. Gleich, D.: Matlabbgl. a matlab graph library. Institute for Computational and Mathematical Engineering, Stanford University (2008)

    Google Scholar 

  14. Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley (1989)

    Google Scholar 

  15. Gori, F., Folino, G., Jetten, M.S.M., Elena Marchiori. Mtr: taxonomic annotation of short metagenomic reads using clustering at multiple taxonomic ranks. Bioinformatics 27(2), 196–203 (2010)

    Google Scholar 

  16. Heeb, H., Ruehli, A.E., Eric Bracken, J., Rohrer, R.A.: Three dimensional circuit oriented electromagnetic modeling for vlsi interconnects. In: IEEE 1992 International Conference on Computer Design: VLSI in Computers and Processors, 1992. ICCD’92. Proceedings, pp. 218–221. IEEE (1992)

    Google Scholar 

  17. Isidori, A.: Nonlinear Control Systems. Springer Science & Business Media, Berlin (2013)

    Google Scholar 

  18. Király, A., Vathy-Fogarassy, Á., Abonyi, J.: Geodesic distance based fuzzy c-medoid clustering-searching for central points in graphs and high dimensional data. Fuzzy Sets Syst. 286, 157–172 (2016)

    Article  MathSciNet  Google Scholar 

  19. Leitold, D., Vathy-Fogarassy, Á., Abonyi, J.: Controllability and observability in complex networks-the effect of connection types. Sci. Rep. 7, 151 (2017)

    Article  Google Scholar 

  20. Leitold, D., Vathy-Fogarassy, A., Abonyi, J.: Design-oriented structural controllability and observability analysis of heat exchanger networks. Chem. Eng. Trans. 70, 595–600 (2018)

    MATH  Google Scholar 

  21. Leitold, D., Vathy-Fogarassy, Á., Abonyi, J.: Network distance-based simulated annealing and fuzzy clustering for sensor placement ensuring observability and minimal relative degree. Sensors 18(9), 3096 (2018)

    Google Scholar 

  22. Liu, X., Mo, Y., Pequito, S., Sinopoli, B., Kar, S., Aguiar, A.P.: Minimum robust sensor placement for large scale linear time-invariant systems: a structured systems approach. IFAC Proc. Vol. 46(27), 417–424 (2013)

    Google Scholar 

  23. Liu, Y.-Y., Slotine, J.-J., Barabási, A.-L.: Observability of complex systems. Proc. Natl. Acad. Sci. 110(7), 2460–2465 (2013)

    Article  MathSciNet  Google Scholar 

  24. Mamano, N., Hayes, W.: Sana: simulated annealing network alignment applied to biological networks (2016). arXiv:1607.02642

  25. Martin, O.C., Otto, S.W.: Combining simulated annealing with local search heuristics. Ann. Oper. Res. 63(1), 57–75 (1993)

    Google Scholar 

  26. Odabasioglu, A., Celik, M., Pileggi, L.T.: Prima: passive reduced-order interconnect macromodeling algorithm. In: Proceedings of the 1997 IEEE/ACM International Conference on Computer-Aided Design, pp. 58–65. IEEE Computer Society (1997)

    Google Scholar 

  27. Papadimitriou, C., Lombaert, G.: The effect of prediction error correlation on optimal sensor placement in structural dynamics. Mech. Syst. Signal Process. 28, 105–127 (2012)

    Article  Google Scholar 

  28. Papadopoulou, M., Raphael, B., Smith, I.F.C., Sekhar, C.: Hierarchical sensor placement using joint entropy and the effect of modeling error. Entropy 16(9), 5078–5101 (2014)

    Google Scholar 

  29. Qin, B.Y., Lin, X.K.: Optimal sensor placement based on particle swarm optimization. In: Advanced Materials Research, vol. 271, pp. 1108–1113. Trans Tech Publications (2011)

    Google Scholar 

  30. Rosich, A., Sarrate, R., Puig, V., Escobet, T.: Efficient optimal sensor placement for model-based fdi using an incremental algorithm. In: 46th IEEE Conference on Decision and Control, pp. 2590–2595. IEEE (2007)

    Google Scholar 

  31. Samora, I., Franca, M.J., Schleiss, A.J., Ramos, H.M.: Simulated annealing in optimization of energy production in a water supply network. Water Res. Manag. 30(4), 1533–1547 (2016)

    Google Scholar 

  32. Padula, S.L.: and Kincaid Rex K. Optimization strategies for sensor and actuator placement. Technical report, National Aeronautics and Space Administration, NASA (1999)

    Google Scholar 

  33. Torres-Jimenez, J., Izquierdo-Marquez, I., Garcia-Robledo, A., Gonzalez-Gomez, A., Bernal, J., Kacker, R.N.: A dual representation simulated annealing algorithm for the bandwidth minimization problem on graphs. Inf. Sci. 303, 33–49 (2015)

    Google Scholar 

  34. Varga, E.I., Hangos, K.M., Szigeti, F.: Controllability and observability of heat exchanger networks in the time-varying parameter case. Control Eng. Pract. 3(10), 1409–1419 (1995)

    Google Scholar 

  35. Watson, J-P., Hart, W.E., Berry, J.W.: Scalable high-performance heuristics for sensor placement in water distribution networks. In: Impacts of Global Climate Change, pp. 1–12. American Society of Civil Engineers (2005)

    Google Scholar 

  36. Westphalen, D.L., Young, B.R., Svrcek, W.Y.: A controllability index for heat exchanger networks. Ind. Eng. Chem. Res. 42(20), 4659–4667 (2003)

    Google Scholar 

  37. Worden, K., Burrows, A.P.: Optimal sensor placement for fault detection. Eng. Struct. 23(8), 885–901 (2001)

    Article  Google Scholar 

  38. Yuen, K.-V., Kuok, S.-C.: Efficient bayesian sensor placement algorithm for structural identification: a general approach for multi-type sensory systems. Earthq. Eng. Struct. Dyn. 44(5), 757–774 (2015)

    Article  Google Scholar 

  39. Zhang, X.-X., Li, H.-X., Qi, C.-K.: Spatially constrained fuzzy-clustering-based sensor placement for spatiotemporal fuzzy-control system. IEEE Trans. Fuzzy Syst. 18(5), 946–957 (2010)

    Article  Google Scholar 

  40. Xun, Z., Juelong, L., Jianchun, X., Ping, W., Qiliang, Y., Ronghao, W., Can, H.: Optimal sensor placement for latticed shell structure based on an improved particle swarm optimization algorithm. Math. Probl. Eng. 2014 (2014)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Systems Technology, University of Pannonia, Veszprém, Hungary

    Dániel Leitold & Ágnes Vathy-Fogarassy

  2. MTA-PE Lendület Complex Systems Monitoring Research Group, University of Pannonia, Veszprém, Hungary

    János Abonyi

Authors
  1. Dániel Leitold
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ágnes Vathy-Fogarassy
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. János Abonyi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dániel Leitold .

Rights and permissions

Reprints and permissions

Copyright information

© 2020 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Leitold, D., Vathy-Fogarassy, Á., Abonyi, J. (2020). Reduction of Relative Degree by Optimal Control and Sensor Placement. In: Network-Based Analysis of Dynamical Systems. SpringerBriefs in Computer Science. Springer, Cham. https://doi.org/10.1007/978-3-030-36472-4_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-36472-4_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-36471-7

  • Online ISBN: 978-3-030-36472-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation