Predictive/Adaptive Control of Complex Systems Using Neural Engineering Techniques
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- Articles
- Submitted: October 20, 2017
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Published: March 15, 2018
Abstract
The design and implementation of a predictive/adaptive control system is presented, using neural engineering techniques to control a non-linear MIMO system in order to control, at a later stage, the temperature and level in a non-linear conical plant. Preliminarily, conventional control structures were tested, which gave rise to the need to test intelligent control structures that allow the control objectives to be met more effectively. The process begins with the experimentation of different neuronal control structures, and then escalates to a predictive/adaptive neuronal control system. The results achieved at the simulation level, testing the proposed design on mathematical models of non-linear MIMO systems, were satisfactory and met the control objectives established, therefore, in the next stage of the project, the experimentation is estimated in the real plant under study.
References
[1] A. Conradie, C. Aldrich, “Neurocontrol of a multi-effect batch distillation pilot plant based on evolutionary reinforcement learning,” Chemical Engineering Science, vol. 65, N.° 5, pp. 1627-1643, 2010.
[2] M. Bazaraa, H. Sherali, C.M. Shetty, Nonlinear programming: theory and Algorithms, 3.a ed., Nueva Jersey: Wiley Interscience, 2006, pp. 872.
[3] S. Chen, S. A. Billings, “Representations of non-linear systems: the NARMAX model,” International Journal of Control, vol. 49, N.° 3, pp. 1013-1032, 1988.
[4] H. González, M.S. Dutra, O. Lengerke, “Identification and modeling for non-linear dynamic system using neural networks type MLP,” presentado en Proceedings of the 2009 Euro American Conference on Telematics and Information Systems: New Opportunities to increase Digital Citizenship, Praga, junio 03-05, 2009.
[5] R. Hecht-Nielsen, Neurocomputing, Boston: Ed. Addison Wesley, 1988, pp. 433.
[6] J. Vojtesek, P. Dostal, “Adaptive control of water level in real model of water tank, Process Control (PC),” presentado en 20th International Conference on, Strbske Pleso, Eslovaquia, junio 9-12, 2015.
[7] A. U. Levin y K. Narendra, “Control of nonlinear dynamical systems using neural networks,” IEEE Neural Networks Council, vol.7, pp. 30-42, 1996.
[8] K. Narendra y K. Parthasarathy, “Identification and Control of Dynamical Systems Using Neural Networks,” IEEE Transactions on Neural Networks, vol. 7, N.° 1, 1996.
[9] H. M. Nguyen y N. Subbaram, “Advanced control strategies for wind energy systems: An overview”, presentado en IEEE/PES Power Systems Conference and Exposition, Phoenix, 2011.
[10] K.J. Nidhil, S. Sreeraj, B. Vijay y V. Bagyaveereswaran, “System identification using artificial neural network”, Circuit, Power and Computing Technologies (ICCPCT), presentado en 2015 International Conference, Nagercoil, 2015.
[11] M. Nørgaard, O. Ravn, NK. Poulsen y LK Hansen, Neural Networks for Modelling and Control of Dynamic Systems, Londres: Springer, 2000, pp. 246.
[12] K. Ogata, Ingeniería de control moderna, 4.a ed., Madrid: Prentice Hall, 2003, pp. 984.
[13] D. T. Pham y L. Xing, Neural Networks for identification, prediction and control, Londres: Springer, 2012, pp. 238.
[14] A. Kupin, “Application of neurocontrol principles and classification optimisation in conditions of sophisticated technological processes of beneficiation complexes”. Metallurgical y Mining Industry, vol. 6, pp. 16-24, 2014.
[15] R.J. Rajesh, R. Preethi, P. Mehata y B. Jaganatha Pandian, “Artificial neural network based inverse model control of a nonlinear process,” presentado en Computer, Communication and Control (IC4), International Conference, Indore, 2015.
[16] V.R. Ravi, M. Monica, S. Amuthameena, S.K. Divya, S. Jayashree y J. Varshini, “Sliding Mode Controller for Two Conical Tank Interacting Level System,” Applied Mechanics and Materials, vol. 573, pp. 273-278, 2014.
[17] A. M. Suárez, Nueva arquitectura de control predictivo para sistemas dinámicos no lineales usando redes neuronales, Tesis de Doctorado en Ciencias de la Ingeniería, Universidad de Chile, Santiago de Chile, 1998.
[18] D. Zhao, Z. Xia y D. Wang, “Model-Free Optimal Control for Affine Nonlinear Systems with Convergence Analysis”, IEEE Transactions on Automation Science and Engineering, vol. 12, pp. 1461-1468, 2015.
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