Abstract
Identification and modeling of systems are the first stage for development and design of controllers. For this purpose, as an alternative to conventional modeling approaches we propose using two methods of evolutionary computing: Genetic Algorithms (GA) and Particle Swarm Optimization (PSO to create an algorithm for modeling Linear Time Invariant (LTI) systems of different types. Integral Square Error (ISE) is the objective function to minimize, which is calculated between the outputs of the real system and the model. Unlike other works, the algorithms make a search of the most approximate model based on four of the most common ones found in industrial processes: systems of first order, first order plus time delay, second order and inverse response. The estimated models by our algorithms are compared with the obtained by other analytical and heuristic methods, in order to validate the results of our approach.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Smith, C., Corripio, A.: Principles and Practice of Automatic Process Control, 3rd edn. Wiley, New York (2006)
Johnson, M., Moradi, M.: PID Control - New Identification and Design Methods. Springer, London (2005). https://doi.org/10.1007/1-84628-148-2
Kristinsson, K., Dumont, G.A.: System identification and control using genetic algorithms. IEEE Trans. Syst. Man. Cybern. 22(5), 1033–1046 (1992)
Johnson, T., Husbands, P.: System identification using genetic algorithms. In: Parallel Problem Solving from Nature, no. 1, pp. 85–89. Springer, Heidelberg (1991)
Zhang, R., Tao, J.: A nonlinear fuzzy neural network modeling approach using an improved genetic algorithm. IEEE Trans. Ind. Electron. 65(7), 5882–5892 (2018)
Alfi, A., Modares, H.: System identification and control using adaptive particle swarm optimization. Appl. Math. Model. 35(3), 1210–1221 (2011)
Dub, M., Stefek, A.: Mechatronics 2013. Springer, Cham (2014)
Hassan, R., Cohanim, B., de Weck, O., Venter, G.: A comparison of particle swarm optimization and the genetic algorithm. In: 46th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference, no. April, pp. 1–13 (2005)
Marlin, T.: Process Control. Design Processes and Control System for Dynamic Performance. McGraw Hill, New York (1995)
Balaguer, P., Alfaro, V., Arrieta, O.: Second order inverse response process identification from transient step response. ISA Trans. 50(2), 231–238 (2011)
Aguilar, J., Cerrada, M.: Genetic programming-based approach for system identification. Adv. Fuzzy Syst. Evol. Comput. Artif. Intell. 329–324 (2001)
Carabalí, C.A., Tituaña, L., Aguilar, J., Camacho, O., Chavez, D.: Inverse response systems identification using genetic programming. In: Proceedings of the 14th International Conference on Informatics in Control, Automation and Robotics, vol. 1, no. Icinco, pp. 238–245 (2017)
Tang, H., Xue, S., Fan, C.: Differential evolution strategy for structural system identification. Comput. Struct. 86(21–22), 2004–2012 (2008)
Venter, G., Sobieszczanski-Sobieski, J.: Particle swarm optimization. AIAA J. 41(8), 1583–1589 (2003)
Aguilar, J.: The evolutionary programming in the identification of discreet events dynamic systems. IEEE Lat. Am. Trans. 5(5), 301–310 (2007)
Garnier, H., Mensler, M., Richard, A.: Continuous-time model identification from sampled data: implementation issues and performance evaluation. Int. J. Control 76(13), 1337–1357 (2003)
Seborg, D., Edgar, T., Mellichamp, D., Doyle, F.: Process Dynamics and Control, 3rd edn. Wiley, New York (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Morales, L., Camacho, O., Chávez, D., Aguilar, J. (2019). An Evolutionary Intelligent Approach for the LTI Systems Identification in Continuous Time. In: Botto-Tobar, M., Pizarro, G., Zúñiga-Prieto, M., D’Armas, M., Zúñiga Sánchez, M. (eds) Technology Trends. CITT 2018. Communications in Computer and Information Science, vol 895. Springer, Cham. https://doi.org/10.1007/978-3-030-05532-5_32
Download citation
DOI: https://doi.org/10.1007/978-3-030-05532-5_32
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-05531-8
Online ISBN: 978-3-030-05532-5
eBook Packages: Computer ScienceComputer Science (R0)