Nonlinear System Identification using Uncoupled State Multi-model Approach: Application to the PCB Soldering System

Authors

  • S. Khouni Department of Electrical Engineering , Ferhat Abbas Setif 1 University, Algeria
  • K. E. Hemsas Department of Electrical Engineering , Ferhat Abbas Setif 1 University, Algeria
Volume: 10 | Issue: 1 | Pages: 5221-5227 | February 2020 | https://doi.org/10.48084/etasr.3247

Abstract

Multi-model approach is an adapted tool of modeling nonlinear systems. The underlying idea is to simplify the complex nature of the system to be studied by decomposing it into simple (linear) sub-systems, in order to simplify the study (stability, control law, surveillance, etc.). This technique allows us to extend the application of linear systems methodology to nonlinear systems. This paper presents nonlinear system identification using an uncoupled state multi-model applied to a Printed Circuit Boards (PCB) soldering system. Precision, simplicity, and fidelity of the obtained results show the effectiveness of the used algorithm to identify, model, and write down as simple sub-systems, a complex black box system.

Keywords:

nonlinear system, identification, uncoupled state, multi-model, profile modeling

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References

T. Takagi, M. Sugeno, “Fuzzy identification of systems and its applications to modelling and control”, IEEE Transactions On Systems, Man and Cybernetics, Vol. 15, No. 1, pp. 116–132, 1985 DOI: https://doi.org/10.1109/TSMC.1985.6313399

D. Filev, “Fuzzy modeling of complex systems”, International Journal of Approximate Reasoning, Vol. 5 No. 3, pp. 281–290, 1991 DOI: https://doi.org/10.1016/0888-613X(91)90013-C

T. A. Johansen, B. A. Foss, “Non linear local model representation for adaptive systems”, Singapore International Conference on Intelligent Control and Instrumentation, Singapore, February 17-21, 1992

T. A. Johansen, B. A. Foss, “Constructing NARMAX using ARMAX”, International Journal of Control, Vol. 58, No. 5, pp. 1125–1153, 1993 DOI: https://doi.org/10.1080/00207179308923046

K. Gasso, Identification des systemes dynamiques non-lineaires: approche multi-modele, PhD Thesis, National Polytechnic Institute of Lorraine, 2000 (in French)

J. K. Gugaliya, R. D. Gudi, S. Lakshminarayanan, “Multi-model decomposition of nonlinear dynamics using a fuzzy-CART approach”, Journal of Process Control, Vol. 15, No. 4, pp. 417–434, 2005 DOI: https://doi.org/10.1016/j.jprocont.2004.07.004

H. Elaggoune, M. Benouaret, M. Messaadia, “Modeling and fault diagnosis sensor by multi-model approach”, 10th International Confernece on Conception and Integrated Production, Tanger, Morroco, December 2-4, 2015

R. Orjuela, B. Marx, J. Ragot, D. Maquin, “Nonlinear system identification using heterogeneous multiple models”, International Journal of Applied Mathematics and Computer Science, Vol. 23, No. 1, pp. 103-105, 2013 DOI: https://doi.org/10.2478/amcs-2013-0009

Z. Lendek, T. M. Guerra, R. Babuska, B. De Schutter, Stability analysis and nonlinear observer design using Takagi-Sugeno fuzzy models, Springer-Verlag, 2010 DOI: https://doi.org/10.1007/978-3-642-16776-8

H. Bedoui, A. Kedher, K. Ben Othman, “Fault detection and isolation for an uncertain Takagi-Sugeno fuzzy system using the interval approach”, in: Handbook of Research on Advanced Intelligent Control Engineering and Automation. IGI Global, 2015 DOI: https://doi.org/10.4018/978-1-4666-7248-2.ch013

K. Tanaka, H. O. Wang, Fuzzy control systems design and analysis: A linear matrix inequality approach, John Wiley & Sons, 2001

A. Akhenak, Conception d’observateurs non lin´eaires par approche multimod`ele: application au diagnostic, PhD Thesis, National Polytechnic Institute of Lorraine, 2004 (in French)

M. Chadli, P. Borne, Multiple models approach in automation: Takagi-Sugeno fuzzy systems, John Wiley & Sons, 2012 DOI: https://doi.org/10.1002/9781118577325

R. Isermann, M. Munchhof, Identification of dynamic systems: an introduction with applications, Springer-Verlag, 2011 DOI: https://doi.org/10.1007/978-3-540-78879-9

D. W. Marquardt, “An algorithm for least-squares estimation of non-linear parameters”, Journal of the Society for Industrial and Applied Mathematics, Vol. 11, No. 2, pp. 431–441, 1963 DOI: https://doi.org/10.1137/0111030

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How to Cite

[1]
S. Khouni and K. E. Hemsas, “Nonlinear System Identification using Uncoupled State Multi-model Approach: Application to the PCB Soldering System”, Eng. Technol. Appl. Sci. Res., vol. 10, no. 1, pp. 5221–5227, Feb. 2020.

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