Published Mar 16, 2012



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José Danilo Rairan-Antoniles, MSc

Diego Fernando Chiquiza-Quiroga

Miguel Ángel Parra-Pachón

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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

Redes neurales, sistemas de control en tiempo real, sistemas híbridos, propagación inversa (inteligencia artificial).Neural networks, real-time control, hybrid systems, back propagation (artificial intelligence).

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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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