Artificial Neural Networks Models Based on ARX and State Space Forms and Adaptive Control PID/LQR of Systems Based on Peltier Cells

Authors

DOI:

https://doi.org/10.31686/ijier.vol9.iss11.3540

Keywords:

Adaptive Control, Neural Networks, System Identification, Thermal System, ltier Cell actuator, Control Design, LQR

Abstract

To improve the performance of a thermal plant based on Peltier cell actuators, an online parametric estimation via artificial neural networks and adaptive controller is presented. The control actions  are based on adaptive digital controller and an adaptive quadratic linear regulator approaches. The Artificial neural networks topology is based on ARX and NARX models, and its training algorithms, such as accelerated backpropagation and recursive least square. The Control strategies are design-oriented to adaptive digital PID controller and quadratic linear regulator framework. The proposal is evaluated on  temperature control of an object that is inside of a chamber.

Downloads

Download data is not yet available.

Author Biographies

Denis Fabricio Sousa De Sá, Federal University of Maranhão

Department of Electrical Engineering-Balsas

João Viana Fonseca Neto, Federal University of Maranhão

Professor, Department of Electrical Engineering and PPGE

References

F. Pasqualetti, F. Dorfler, and F. Bullo, “Attack detection and identification in cyber-physical systems,” Automatic Control, IEEE Transactions on, vol. 58, no. 11, pp. 2715–2729, 2013. DOI: https://doi.org/10.1109/TAC.2013.2266831

L.-Z. Liu, F.-X. Wu, L.-L. Han, and W. Zhang, “Structure identification and parameter estimation of biological s-systems,” in Bioinformatics and Biomedicine (BIBM), 2010 IEEE International Conference on, 2010, pp. 329–334. DOI: https://doi.org/10.1109/BIBM.2010.5706586

P. Z. Marmarelis and K.-i. Naka, “Identification of multi-input biological systems,” Biomedical Engineering, IEEE Transactions on, vol. BME-21, no. 2, pp. 88–101, 1974. DOI: https://doi.org/10.1109/TBME.1974.324293

C.-N. Ko,“Identification of non-linear systems using radial basis function neural networks with time-varying learning algorithm,” Signal Processing, IET, vol. 6, no. 2, pp. 91–98, 2012. DOI: https://doi.org/10.1049/iet-spr.2011.0025

C. Turchetti, F. Gianfelici, G. Biagetti, and P. Crippa, “A computational intelligence technique for the identification of non-linear non-stationary systems,” in Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence). IEEE International Joint Conference on, 2008, pp. 3034–3038. DOI: https://doi.org/10.1109/IJCNN.2008.4634226

F. Jurado, M. Flores, V. Santibanez, M. Llama, and C. Castaneda,“Continuous-time neural identification for a 2 dof vertical robot manipulator,”in Electronics, Robotics and Automotive Mechanics Conference (CERMA), 2011 IEEE, Nov 2011, pp. DOI: https://doi.org/10.1109/CERMA.2011.20

–82.

I. D. Landau, Digital Control Systems, A. J. E.D.Sontag, M.Thoma, Ed. Springer, 1938.

S. Haykin, Redes Neurais - 2ed. BOOKMAN COMPANHIA ED, 2001.

Xiao, F. Long, and Y. Zhao,“Based on elm forged neural control for a class of strict feedback stochastic nonlinear switched system with time varying delay,”in Control Conference (CCC), 2013 32nd Chinese, 2013, pp. 2168–2173.

S. Nirkhi, “Potential use of artificial neural network in data mining,” in Computer and Automation Engineering (ICCAE), 2010 The 2nd International Conference on, vol. 2, 2010, pp. 339–343. DOI: https://doi.org/10.1109/ICCAE.2010.5451537

I. Baruch, S. Hernandez, S. Echeverria, and O. Castillo,“Decentralized direct and indirect i-term adaptive fuzzy-neural control of a bioprocess plant,” in Fuzzy Information Processing Society (NAFIPS), 2012 Annual Meeting of the North American, 2012, pp. 1–8. DOI: https://doi.org/10.1109/NAFIPS.2012.6291050

S. Haykin, Neural Networks, A Comprehensive Foundation, 2nd ed. Prentice Hall International, 1999.

J. R. Raol and H. Madhuranath,“Neural network architectures for parameter estimation of dynamical systems,”Control Theory and Applications, IEE Proceedings -, vol. 143, no. 4, pp. 387– 394, Jul 1996. DOI: https://doi.org/10.1049/ip-cta:19960338

R. Bharadwaj, A. Parlos, and H. Toliyat, “Adaptive neural network-based state filter for induction motor speed estimation,” in Industrial Electronics Society, 1999. IECON ’99 Proceedings. The 25th Annual Conference of the IEEE, vol. 3, 1999, pp. 1283– 1288 vol.3.

J. V. G. Pezzotti and Londono, “Discrete deadbeat control of a plant pressure through identification by neural networks,”IEEE Laton America Transactions, 2012.

A. Janczak, Identification of Nonlinear Systems Using Neural Networks and Polynomial Models: A Block-Oriented Approach, ser. Lecture Notes in Control and Information Sciences. Springer, 2004. DOI: https://doi.org/10.1007/b98334

L. Leithold, O ca´lculo com geometria anal´ıtica, 3rd ed. Harbra, 1994, no. v. 2.

Y. Nesterov, “A method of solving a convex programming problem with convergence rate o (1/k2),” Soviet Mathematics Doklady, vol. 27, no. 2, pp. 372–376, 1983.

P. Patrinos and A. Bemporad, “An accelerated dual gradientprojection algorithm for embedded linear model predictive control,” Automatic Control, IEEE Transactions on, vol. 59, no. 1, pp. 18–33, Jan 2014. DOI: https://doi.org/10.1109/TAC.2013.2275667

S. Ghadimi and G. Lan,“Accelerated gradient methods for nonconvex nonlinear and stochastic programming,” arXiv preprint arXiv:1310.3787, 2013.

K. Astrom and B. Wittenmark, Adaptive Control: Second Edition, ser. Dover Books on Electrical Engineering. Dover Publications, 2008.

L. d. A. H. N. A.M.N. Lima, G.S. Deep and M. Fontana, “A gain-scheduling pid-like controller for peltier-based thermal hysteresis characterization platform,” IEEE Instrumentation and Measurement Technology Conference, vol. may, 2001.

F. L. Lewis, Applied Optimal Control & Estimation - Digital Design & Implementation, 1st ed., ser. Digital signal Processing.New Jersey: Prentice Hall, 1992, vol. 17.

M. Norgaard at all, Neural Networks for modelling and Control of Dynamic Systems, Springer London, 2000.

Avery, R. J., Bryant, W. K., Mathios, A., Kang, H., & Bell, D. (2006). Electronic course evaluations: Does an online delivery system influence student evaluations? The Journal of Economic Education, 37(1), 21–37. https://doi.org/10.3200/JECE.37.1.21-37 DOI: https://doi.org/10.3200/JECE.37.1.21-37

Berk, R. A. (2012). Top 20 strategies to increase the online response rates of student rating scales. International Journal of Technology in Teaching and Learning, 8(2), 98–107.

Downloads

Published

01-11-2021

How to Cite

Sousa De Sá, D. F., & Fonseca Neto, J. V. (2021). Artificial Neural Networks Models Based on ARX and State Space Forms and Adaptive Control PID/LQR of Systems Based on Peltier Cells. International Journal for Innovation Education and Research, 9(11), 455–477. https://doi.org/10.31686/ijier.vol9.iss11.3540

Most read articles by the same author(s)