The implementation of on-line neurocontrollers: three configurations, three plants
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Mar 16, 2012
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Relación de los cambios (Español (España))
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Implementación de neurocontroladores en lÃnea. Tres configuraciones, tres plantas (Español (España))
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versión con 12 páginas (Español (España))
artÃculo corregido 12 páginas (Español (España))
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Abstract
In this paper we develop a backpropagation learning algorithm for feedforward neural networks trained online. Three neurocontrollers are designed for three systems. Those systems are an RC circuit, a DC motor (electronically emulated) and a spheretube system. The first implemented strategy is a standard PID controller, which is used in order to compare the performance of the neurocontrollers. The first neurocontroller leads the system in parallel with a PID; the next one is trained online to work alone, and the last one is a neural PID, which strives to make the controller adaptable to the dynamics of the plant trough changes on the PID gains. The control is carried out in real time by using Simulink and a PCI 6024E data acquisition card. The results for each system are also included.
Keywords
Neural networks, real-time control, hybrid systems, back propagation (artificial intelligence).Redes neurales, sistemas de control en tiempo real, sistemas híbridos, propagación inversa (inteligencia artificial).
References
GUO, C.; SONG, Q. y CAI, W. A Neural Network Assisted Cascade Control System for Air Handling Unit. IEEE Transactions on Industry Applications. 2007, vol. 54, núm. 1, pp. 620-628.
HEDJAR, R. Online adaptive control of non-linear plants using neural networks with application to temperature control system. Journal of King Saud University, Computer & Information Sciences. 2007, vol. 19, núm. 1, pp. 75-94.
KADWANE, S.; KUMAR, A.; KARAN B. y GHOSE T. Online trained simulation and DSP Implementation of dynamic back propagation neural network for buck converter. ACSE Journal. 2006, vol. 6, núm. 1, pp. 27-34.
KAMALASADAN, S. y GHANDAKLY, A. A neural network parallel adaptive controller for dynamic system control. IEEE Transactions on Instrumentation and Measurement. 2007, vol. 56, núm. 5, pp. 1786-1796.
NOH, J.; LEE, G. y JUNG, S. Motion control of a mobile pendulum system using neural network. Advanced Motion Control, 2008. AMC ‘08. 10th IEEE International Workshop. 2008, pp. 450-454.
NOURI, K.; DHAOUADI, R. y BRAIEK, N. Nonlinear speed control of a dc motor drive system with online trained recurrent neural network. 9th IEEE international workshop on advanced motion control. 2006, pp. 704-708.
OMATU, S. Neuro-control and its applications to electric vehicle control. IWANN’09 Proceedings of the 10th Interntaional Work-Conference on Aritificial Neural Networks: Part II: Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living. 2009, pp. 1-12.
OUYANG, Z.; SCHNELL, M. y WEI, K. The experiment Ball-in-tube with Fuzzy-PID controller based on space. IEEE International Conference on Systems, Man and Cybernetics. 2007, pp. 877-881.
POPESCU, M.; BALAS, V.; MUSCA, S. y COSMA, D. Modeling the phenomenon of refreshing air inside the spaces of the Fungi’s culture. ISCIII 2009, 4th International Symposium on Computational Intelligence and Intelligent Informatics. 2009, Egipto, pp. 85-92.
RAIRÁN, J. y FONSECA, J. Doble lazo de control para regular la posición y la velocidad en un motor de corriente directa. Ingeniería y Universidad. 2011, vol. 15, núm. 2, pp. 337-357.
RAIRÁN, J.; PÉREZ, J. y OSORIO, J. Implementación de una red neuronal para la medición indirecta de posición. Revista Avances en Sistemas e Informática. 2009, vol. 6, núm. 3, pp. 79-85.
RUBAAI, A.; KOTARU, R. y KANKAM, M. Online training of parallel neural network estimators for control of induction motors. IEEE Transactions on Industry Applications. 2001, vol. 37, núm. 5, pp. 1512-1521.
WEERASOORIYA, S. y EL-SHARKAWI, M. Laboratory implementation of a neural network trajectory controller for a DC motor. IEEE Transactions on Energy Conversion. 1993, vol. 8, núm. 1, pp. 107-113.
HEDJAR, R. Online adaptive control of non-linear plants using neural networks with application to temperature control system. Journal of King Saud University, Computer & Information Sciences. 2007, vol. 19, núm. 1, pp. 75-94.
KADWANE, S.; KUMAR, A.; KARAN B. y GHOSE T. Online trained simulation and DSP Implementation of dynamic back propagation neural network for buck converter. ACSE Journal. 2006, vol. 6, núm. 1, pp. 27-34.
KAMALASADAN, S. y GHANDAKLY, A. A neural network parallel adaptive controller for dynamic system control. IEEE Transactions on Instrumentation and Measurement. 2007, vol. 56, núm. 5, pp. 1786-1796.
NOH, J.; LEE, G. y JUNG, S. Motion control of a mobile pendulum system using neural network. Advanced Motion Control, 2008. AMC ‘08. 10th IEEE International Workshop. 2008, pp. 450-454.
NOURI, K.; DHAOUADI, R. y BRAIEK, N. Nonlinear speed control of a dc motor drive system with online trained recurrent neural network. 9th IEEE international workshop on advanced motion control. 2006, pp. 704-708.
OMATU, S. Neuro-control and its applications to electric vehicle control. IWANN’09 Proceedings of the 10th Interntaional Work-Conference on Aritificial Neural Networks: Part II: Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living. 2009, pp. 1-12.
OUYANG, Z.; SCHNELL, M. y WEI, K. The experiment Ball-in-tube with Fuzzy-PID controller based on space. IEEE International Conference on Systems, Man and Cybernetics. 2007, pp. 877-881.
POPESCU, M.; BALAS, V.; MUSCA, S. y COSMA, D. Modeling the phenomenon of refreshing air inside the spaces of the Fungi’s culture. ISCIII 2009, 4th International Symposium on Computational Intelligence and Intelligent Informatics. 2009, Egipto, pp. 85-92.
RAIRÁN, J. y FONSECA, J. Doble lazo de control para regular la posición y la velocidad en un motor de corriente directa. Ingeniería y Universidad. 2011, vol. 15, núm. 2, pp. 337-357.
RAIRÁN, J.; PÉREZ, J. y OSORIO, J. Implementación de una red neuronal para la medición indirecta de posición. Revista Avances en Sistemas e Informática. 2009, vol. 6, núm. 3, pp. 79-85.
RUBAAI, A.; KOTARU, R. y KANKAM, M. Online training of parallel neural network estimators for control of induction motors. IEEE Transactions on Industry Applications. 2001, vol. 37, núm. 5, pp. 1512-1521.
WEERASOORIYA, S. y EL-SHARKAWI, M. Laboratory implementation of a neural network trajectory controller for a DC motor. IEEE Transactions on Energy Conversion. 1993, vol. 8, núm. 1, pp. 107-113.
How to Cite
Rairan-Antoniles, J. D., Chiquiza-Quiroga, D. F., & Parra-Pachón, M. Ángel. (2012). The implementation of on-line neurocontrollers: three configurations, three plants. Ingenieria Y Universidad, 16(1), 163. https://doi.org/10.11144/Javeriana.iyu16-1.inlt
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