article

A SVR Learning Based Sensor Placement Approach for Nonlinear Spatially Distributed Systems

Authors:

Xian-xia Zhang,

Tao ZouAuthors Info & Claims

Applied Computational Intelligence and Soft Computing, Volume 2016

Page 2

https://doi.org/10.1155/2016/5241279

Published: 01 November 2016 Publication History

Abstract

Many industrial processes are inherently distributed in space and time and are called spatially distributed dynamical systems SDDSs. Sensor placement affects capturing the spatial distribution and then becomes crucial issue to model or control an SDDS. In this study, a new data-driven based sensor placement method is developed. SVR algorithm is innovatively used to extract the characteristics of spatial distribution from a spatiotemporal data set. The support vectors learned by SVR represent the crucial spatial data structure in the spatiotemporal data set, which can be employed to determine optimal sensor location and sensor number. A systematic sensor placement design scheme in three steps data collection, SVR learning, and sensor locating is developed for an easy implementation. Finally, effectiveness of the proposed sensor placement scheme is validated on two spatiotemporal 3D fuzzy controlled spatially distributed systems.

References

[1]

Christofides P. D., Nonlinear and Robust Control of Partial Differential Equation Systems: Methods and Applications to Transport-reaction Processes 2001 Boston, Mass, USA Birkhäuser

[2]

Qi C. K., Li H.-X., Li S. Y., Zhao X., Gao F., A fuzzy-based spatio-temporal multi-modeling for nonlinear distributed parameter processes Applied Soft Computing 2014 Volume 25 pp.309 –321

Digital Library

[3]

Li H.-X., Qi C. K., Modeling of distributed parameter systems for applications—a synthesized review from time-space separation Journal of Process Control 2010 Volume 20 Issue 8 pp.891 –901

[4]

Rensfelt A., Mousavi S., Mossberg M., Optimal sensor locations for nonparametric identification of viscoelastic materials Automatica 2008 Volume 44 Issue 1 pp.28 –38

Digital Library

[5]

Ucinski D., Optimal sensor location for parameter estimation of distributed processes International Journal of Control 2000 Volume 73 Issue 13 pp.1235 –1248

[6]

Armaou A., Demetriou M. A., Optimal actuator/sensor placement for linear parabolic PDEs using spatial H2 norm Chemical Engineering Science 2006 Volume 61 Issue 22 pp.7351 –7367

[7]

Alonso A. A., Kevrekidis I. G., Banga J. R., Frouzakis C. E., Optimal sensor location and reduced order observer design for distributed process systems Computers and Chemical Engineering 2004 Volume 28 Issue 1-2 pp.27 –35

[8]

Bruant I., Gallimard L., Nikoukar S., Optimal piezoelectric actuator and sensor location for active vibration control, using genetic algorithm Journal of Sound and Vibration 2010 Volume 329 Issue 10 pp.1615 –1635

[9]

Antoniades C., Christofides P. D., Integrating nonlinear output feedback control and optimal actuator/sensor placement for transport-reaction processes Chemical Engineering Science 2001 Volume 56 Issue 15 pp.4517 –4535

[10]

Antoniades C., Christofides P. D., Integrated optimal actuator/sensor placement and robust control of uncertain transport-reaction processes Computers and Chemical Engineering 2002 Volume 26 Issue 2 pp.187 –203

[11]

Lou Y., Christofides P. D., Optimal actuator/sensor placement for nonlinear control of the kuramoto-sivashinsky equation IEEE Transactions on Control Systems Technology 2003 Volume 11 Issue 5 pp.737 –745

[12]

Wouwer V. A., Point N., Porteman S., Remy M., Approach to the selection of optimal sensor locations in distributed parameter systems Journal of Process Control 2000 Volume 10 Issue 4 pp.291 –300

[13]

Zamprogna E., Barolo M., Seborg D. E., Optimal selection of soft sensor inputs for batch distillation columns using principal component analysis Journal of Process Control 2005 Volume 15 Issue 1 pp.39 –52

[14]

Tongpadungroda P., Rhysb T. D. L., Brettc P. N., An approach to optimise the critical sensor locations in one-dimensional novel distributive tactile surface to maximise performance Sensors and Actuators, A: Physical 2003 Volume 105 Issue 1 pp.47 –54

[15]

Zhang X.-X., Li H.-X., Qi C.-K., Spatially constrained fuzzy-clustering based sensor placement for spatio-temporal fuzzy-control system IEEE Transactions on Fuzzy Systems 2010 Volume 18 Issue 5 pp.946 –957

Digital Library

[16]

Silva R. N., Lemos J. M., Rato L. M., Variable sampling adaptive control of a distributed collector solar field IEEE Transactions on Control Systems Technology 2003 Volume 11 Issue 5 pp.765 –772

[17]

Christofides P. D., Robust control of parabolic PDE systems Chemical Engineering Science 1998 Volume 53 Issue 16 pp.2949 –2965

[18]

Winkin J. J., Dochain D., Ligarius P., Dynamical analysis of distributed parameter tubular reactors Automatica 2000 Volume 36 Issue 3 pp.349 –361

Digital Library

[19]

Hoo K. A., Zheng D., Low-order control-relevant models for a class of distributed parameter systems Chemical Engineering Science 2001 Volume 56 Issue 23 pp.6683 –6710

[20]

Vapnik V. N., Statistical Learning Theory 1998 New York, NY, USA John Wiley & Sons

[21]

Haber R., Keviczky L., Nonlinear System Identification—Input-Output Modeling Approach, Volume 1: Nonlinear System Parameter Identification 1999 Dordrecht, The Netherlands Kluwer Academic Publishers

[22]

Schiesser W. E., The Numerical Methods of Lines Integration of Partial Differential Equations 1991 San Diego, Calif, USA Academic Press

[23]

Li H.-X., Zhang X.-X., Li S.-Y., A three-dimensional fuzzy control methodology for a class of distributed parameter systems IEEE Transactions on Fuzzy Systems 2007 Volume 15 Issue 3 pp.470 –481

Digital Library

[24]

Zhang X.-X., Li H.-X., Li S.-Y., Analytical study and stability design of a 3-D fuzzy logic controller for spatially distributed dynamic systems IEEE Transactions on Fuzzy Systems 2008 Volume 16 Issue 6 pp.1613 –1625

Digital Library

[25]

Kellogg C. B., Zhao F., Influence-based model decomposition for reasoning about spatially distributed physical systems Artificial Intelligence 2001 Volume 130 Issue 2 pp.125 –166

Digital Library

[26]

Zhang X.-X., Li S. Y., Li H.-X., Decomposition-coordination-based fuzzy logic control for spatially distributed systems Control and Decision 2008 Volume 23 Issue 6 pp.709 –713

Cited By

Zhang XZhao LLi JCao GWang B(2017)Space-decomposition based 3D fuzzy control design for nonlinear spatially distributed systems with multiple control sources using multiple single-output SVR learningApplied Soft Computing10.1016/j.asoc.2017.04.06459:C(378-388)Online publication date: 1-Oct-2017
https://dl.acm.org/doi/10.1016/j.asoc.2017.04.064

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cover image Applied Computational Intelligence and Soft Computing

Applied Computational Intelligence and Soft Computing Volume 2016, Issue

November 2016

ISSN:1687-9724

EISSN:1687-9732

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Copyright © Copyright © 2016 Xian-xia Zhang et al.

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Hindawi Limited

London, United Kingdom

Publication History

Published: 01 November 2016

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Cited By

Zhang XZhao LLi JCao GWang B(2017)Space-decomposition based 3D fuzzy control design for nonlinear spatially distributed systems with multiple control sources using multiple single-output SVR learningApplied Soft Computing10.1016/j.asoc.2017.04.06459:C(378-388)Online publication date: 1-Oct-2017
https://dl.acm.org/doi/10.1016/j.asoc.2017.04.064

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