Hostname: page-component-78c5997874-4rdpn Total loading time: 0 Render date: 2024-11-10T13:28:04.977Z Has data issue: false hasContentIssue false
  • English
  • Français

A reinforced combinatorial particle swarm optimization based multimodel identification of nonlinear systems

Published online by Cambridge University Press:  05 December 2016

Ahmed A. Adeniran  and
Sami El Ferik
Ahmed A. Adeniran*
Affiliation:
Systems Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia
Sami El Ferik
Affiliation:
Systems Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia
*
Reprint requests to: Ahmed A. Adeniran, Systems Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia. E-mail: selferik@kfupm.edu.sa
Get access Rights & Permissions [Opens in a new window]

Abstract

Several industrial systems are characterized by high nonlinearities with wide operating ranges and large set point changes. Identification and representation of these systems represent a challenge, especially for control engineers. Multimodel technique is one effective approach that can be used to describe nonlinear systems through the combination of several submodels, where each is contributing to the output with a certain degree of validity. One major concern in this technique, especially for systems with unknown operating conditions, is the partitioning of the system's operating space and thus the identification of different submodels. This paper proposes a three-stage approach to obtain a multimodel representation of a nonlinear system. A reinforced combinatorial particle swarm optimization and hybrid K-means are used to determine the number of submodels and their respective parameters. The proposed method automatically optimizes the number of submodels with respect to the submodel complexity. This allows operating space partition and generation of a parsimonious number of submodels without prior knowledge. The application of this approach on several examples, including a continuous stirred tank reactor, demonstrates its effectiveness.

Keywords

K-MeansMultimodelNonlinear SystemsParticle Swarm OptimizationSystems Identification
Type
Regular Articles
Information
AI EDAM , Volume 31 , Special Issue 3: Uncertainty Quantification for Engineering Design , August 2017 , pp. 327 - 358
Copyright
Copyright © Cambridge University Press 2016 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Abbas, T., & Tufvesson, F. (2011). System identification in gsm/edge receivers using a multi-model approach. ACEEE International Journal on Control System and Instrumentation 3(1), 41–46.Google Scholar
Adeniran, A.A., & Elferik, S. (2014). Validity estimation for multi-model identification using constrained Kalman filte. Proc IASTED on Modeling Identification and Control (MIC), (Hamza, M., Ed.), Innsbruck, Austria, February 17–19.Google Scholar
Bedoui, S., Ltaief, M., & Abderrahim, K. (2012). New method for systematic determination of the model's base of time varying delay system. International Journal of Computer Applications 46(1), 13–19.Google Scholar
Billings, S.A. (2013). Models for Linear and Nonlinear Systems. Hoboken, NJ: Wiley Online Library.Google Scholar
Chen, F.-C., & Khalil, H. (1995). Adaptive control of a class of nonlinear discrete-time systems using neural networks. IEEE Transactions on Automatic Control 40(5), 791–801.Google Scholar
Cornoiu, M., Bara, C., & Popescu, D. (2013). Metaheuristic approach in nonlinear systems identification. UPB Scientific Bulletin, Series A 75(3), 91–104.Google Scholar
da Costa Martins, J.K.E., & de Araujo, F.M.U. (2015). Nonlinear system identification based on modified anfis. Proc. 12th Int. Conf. Informatics in Control, Automation and Robotics (ICINCO), 2015, Vol. 1, pp. 588–595, Colmar, Alsace, France, November 21–23.Google Scholar
Demirli, K., Chengs, S.X., & Muthukumaran, P. (2003). Subtractive clustering based on modeling of job sequencing with parametric search. Fuzzy Sets and Systems 137(2), 235–270.Google Scholar
DeVeaux, R., Psichogios, D., & Ungar, L.H. (1993). A comparison of two nonparametric estimation schemes: Mars and neural networks. Computers in Chemical Engineering 17(8), 819–837.Google Scholar
Du, J., Song, C., & Li, P. (2009). Application of gap metric to model bank determination in multilinear model approach. Journal of Process Control 19(2), 231–240.Google Scholar
Dunlop, M.J., Franco, E., & Murray, R.M. (2007). Multi-model approach to identification of biosynthetic pathways. Proc. 2007 American Control Conf., New York, July 11–13.Google Scholar
Elfelly, N., Dieulot, J., Benrejeb, M., & Borne, P. (2010). A new approach for multi-model identification of complex systems based on both neural and fuzzy clustering algorithms. Engineering Applications of Artificial Intelligence 23(7), 1064–1071.Google Scholar
Galan, O., Romagnoli, J.A., Palazoǧlu, A., & Arkun, Y. (2003). Gap metric concept and implications for multilinear model-based controller design. Industrial & Engineering Chemistry Research 42(10), 2189–2197.CrossRefGoogle Scholar
Gan, L., & Wang, L. (2011). A multi-model approach to predictive control of induction motor. Proc. IECON 2011—37th Annual Conf. IEEE Industrial Electronics Society, pp. 1704–1709, Melbourne, Australia, November 4–7.Google Scholar
Gregorčič, G., & Lightbody, G. (2007). Local model network identification with Gaussian processes. IEEE Transactions on Neural Networks 18(9), 1404–1423.Google Scholar
Gugaliya, J.K., & Gudi, R.D. (2010). Multimodel decomposition of nonlinear dynamics using fuzzy classification and gap metric analysis. Proc. 9th Int. Symp. Dynamics and Control of Process Systems. New York: Elsevier.Google Scholar
Hu, Y.-J., Wang, Y., Wang, Z.-L., Wang, Y.-Q., & Zhang, B.C. (2014). Machining scheme selection based on a new discrete particle swarm optimization and analytic hierarchy process. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 28(2), 71–82.Google Scholar
Jarboui, B., Cheikh, M., Siarry, P., & Rebai, A. (2007). Combinatorial particle swarm optimization (cpso) for partitional clustering problem. Applied Mathematics and Computation 192(2), 337–345.Google Scholar
Kashiwagi, H., & Rong, L. (2002). Identification of volterra kernels of nonlinear van de Vusse reactor. Transactions on Control, Automation, and Systems Engineering 4(2), 109–113.Google Scholar
Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. Proc. IEEE Int. Conf. Neural Networks, 1995, Vol. 4, pp. 1942–1948. Perth, Western Australia, November 27–December 1.Google Scholar
Kramar, D., Cica, D., Sredanovic, B., & Kopac, J. (2015). Design of fuzzy expert system for predicting of surface roughness in high-pressure jet assisted turning using bioinspired algorithms. Artificial Intelligence for Engineering Design, Analysis and Manufacturing. Advance online publication.Google Scholar
Ksouri-Lahmari, M., Borne, P., & Benrejeb, M. (2004). Multimodel: the construction of model bases. Studies in Informatics and Control 13(3), 199–210.Google Scholar
Ljung, L. (Ed.). (1999). System Identification: Theory for the User, 2nd ed. Upper Saddle River, NJ: Prentice Hall.Google Scholar
Ltaief, M., Abderrahim, K., & Ben Abdennour, R. (2008). Contributions to the multimodel approach: systematic determination of a models’ base and validities estimation. Journal of Automation & Systems Engineering 2(3).Google Scholar
Majhi, B., & Panda, G. (2011). Robust identification of nonlinear complex systems using low complexity {ANN} and particle swarm optimization technique. Expert Systems With Applications 38(1), 321–333.Google Scholar
Modares, H., Alfi, A., & Fateh, M.-M. (2010). Parameter identification of chaotic dynamic systems through an improved particle swarm optimization. Expert Systems With Applications 37(5), 3714–3720.Google Scholar
Mohamed, R.B., Ben Nasr, H., & Sahli, F.M. (2011). A multi-model approach for a nonlinear system based on neural network validity. International Journal of Intelligent Computing and Cybernetics 4(3), 331–352.Google Scholar
Narendra, K., & Parthasarathy, K. (1990). Identification and control of dynamical systems using neural networks. IEEE Transactions on Neural Networks 1(1), 4–27.Google Scholar
Nihan, N.L., & Davis, G.A. (1987). Recursive estimation of origin-destination matrices from input/output counts. Transportation Research Part B: Methodological 21(2), 149–163.Google Scholar
Novak, J., Chalupa, P., & Bobal, V. (2009). Local model networks for modelling and predictive control of nonlinear systems. Proc. 23rd European Conf. Modelling and Simulation, Madrid, June 9–12.Google Scholar
Novak, J., Chalupa, P., & Bobal, V. (2011). Modelling and predictive control of nonlinear systems using local model networks. Proc. 18th IFAC Congr., Milan, Italy, August 28–September 2.Google Scholar
Orjuela, R., Maquin, D., & Ragot, J. (2006). Nonlinear system identification using uncoupled state multiple model approach. Workshop on Advanced Control and Diagnosis, ACD'2006, Nancy, France, November 16–17.Google Scholar
Orjuela, R., Marx, B., & Ragot, J., & Maquin, D. (2007). State estimation of nonlinear discrete-time systems based on the decoupled multiple model approach. Proc. 4th Int. Conf. Informatics in Control, Automation and Robotics, Angers, France, May 9–12.Google Scholar
Orjuela, R., Marx, B., Ragot, J., & Maquin, D. (2013). Nonlinear system identification using heterogeneous multiple models. International Journal of Applied Mathematics and Computer Science 23(1), 103–115.Google Scholar
Pan, W., Yuan, Y., Goncalves, J., & Stan, G. (2016). A sparse Bayesian approach to the identification of nonlinear state-space systems. IEEE Transactions on Automatic Control 61(1), 182–187.Google Scholar
Ronen, M., Shabtai, Y., & Guterman, H. (2002). Hybrid modeling building methodology using unsupervised fuzzy clustering and supervised neural networks. Biotechnology and Bioengineering 77(4), 420–429.Google Scholar
Samia, T., Ben Abdennour, R., Kamel, A., & Borne, P. (2002). A systematic determination approach of a models’ base for uncertain systems: experimental validation. Proc. IEEE International Conf. Systems Man and Cybernetics, Vol. 6, pp. 73–81. New York: IEEE.Google Scholar
Samia, T., Kamel, A., Ridha, B.A., & Mekki, K. (2008). Multimodel approach using neural networks for complex systems modeling and identification. Nonlinear Dynamics and Systems Theory 8(3), 299–316.Google Scholar
Scarpiniti, M., Comminiello, D., Parisi, R., & Uncini, A. (2015). Novel cascade spline architectures for the identification of nonlinear systems. IEEE Transaction on Circuits and Systems I: Regular Papers 62(7), 1825–1835.Google Scholar
Shorten, R., Murry-Smith, R., Bjorgan, R., & Gollee, H. (1999). On the interpretation of local models in blended multiple structures. International Journal of Control 72(2), 620–628.Google Scholar
Simon, D. (2006). Kalman Filter Generalizations. Hoboken, NJ: Wiley.Google Scholar
Simon, D. (2010). Kalman filtering with state constraints: a survey of linear and nonlinear algorithms. Control Theory Applications IET 4(8), 1303–1318.Google Scholar
Simon, D., & Chia, T.L. (2002). Kalman filtering with state equality constraints. IEEE Transactions on Aerospace and Electronic Systems 38(1), 128–136.Google Scholar
Stanforth, R., Kolossov, E., & Mirkin, B. (2007). Hybrid k-means: combining regressionwise and centroid-based criteria for qsar. In Selected Contributions in Data Analysis and Classification: Studies in Classification, Data Analysis, and Knowledge Organization (Brito, P., Cucumel, G., Bertrand, P., & Carvalho, F., Eds.), pp. 225–233. Berlin: Springer.Google Scholar
Tang, Y., Qiao, L., & Guan, X. (2010). Identification of wiener model using step signals and particle swarm optimization. Expert Systems With Applications 37(4), 3398–3404.Google Scholar
Teslic, L., Hartmann, B., Nelles, O., & Škrjanc, I. (2011). Nonlinear system identification by Gustafson-Kessel fuzzy clustering and supervised local model network learning for the drug absorption spectra process. IEEE Transactions on Neural Networks 22(12), 1941–1951.Google Scholar
Venkat, A.N., Vijaysai, P., & Gudi, R.D. (2003). Identification of complex nonlinear process based on fuzzy decomposition of the steady state space. Journal of Process Control 13(6), 473–488.Google Scholar
Verdult, V., Ljung, L., & Verhaegen, M. (2002). Identification of composite local linear statespace models using a projected gradient search. International Journal of Control 75(16), 1385–1398.Google Scholar
Wen, C., Wang, S., Jin, X., & Ma, X. (2007). Identification of dynamic systems using piecewise-affine basis function models. Automatica 43(10), 1824–1831.Google Scholar
Wernholt, E., & Moberg, S. (2011). Nonlinear gray-box identification using local models applied to industrial robots. Automatica 47(4), 650–660.Google Scholar
Xue, Z.-K., & Li, S.-Y. (2005). A multi-model identification algorithm based on weighted cost function and application in thermal process. ACTA Automatica, SINICA 31(3), 470–474.Google Scholar
Yassin, I.M., Taib, M.N., & Adnan, R. (2013). Recent advancements and methodologies in system identification: a review. Scientific Research Journal 1(1), 14–33.Google Scholar
Zeng, K., Tan, Z., Dong, M., & Yang, P. (2014). Probability increment based swarm optimization for combinatorial optimization with application to printed circuit board assembly. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 28(11), 429–437.Google Scholar