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Journal of applied research and technology
versión On-line ISSN 2448-6736versión impresa ISSN 1665-6423
J. appl. res. technol vol.10 no.2 Ciudad de México abr. 2012
PID Based on a Single Artificial Neural Network Algorithm for Intelligent Sensors
J. Rivera-Mejía*1, A.G. Léon-Rubio2, E. Arzabala-Contreras3
1,2,3 División de Estudios de Posgrado e Investigación del Instituto Tecnológico de Chihuahua. Av. Tecnológico No. 2909, Chihuahua, Chihuahua. México, 31310; Tel. +52 (614) 413 7474; Fax. +52 (614) 413 5187; *jrivera@itchihuahua.edu.mx.
Abstract
Today control is required in any field or application. Nowadays, classic control is the most used, but it is well-known that users need to know the system's characteristics to reach optimal control. This paper is focused on designing a proportional integral derivative control, based on a single artificial neural network with the aim to improve its performance and its use with minimal control knowledge from the end user. The proposed control was assessed with simulated and practical physical systems of first and second order. In order to increase the confidence of the intelligent sensor control, the evaluation was made using the classical test of control response of a step as input. The proposed control was implemented on an intelligent sensor with a small microcontroller. Also, the performance was compared between the proposed control and a commercial control. Here, an intelligent sensor is presented with control capability for a wide variety of physical systems. The experiments performed demonstrated the capability of the proposed control, which can be easily used and save time at the initial control set up.
Keywords: control, intelligent sensors, intelligent control, artificial neural network.
Resumen
Hoy en día el control es requerido en cualquier área o aplicación. En nuestros días, el control clásico es el más utilizado, pero para alcanzar un control óptimo, éste requiere que los usuarios conozcan las características del sistema a controlar. Este documento está enfocado en el diseño de un control proporcional derivativo basado en una sola red neuronal, con desempeño del control mejorado y que su uso requiera de mínimos conocimientos de control. El control propuesto fue evaluado utilizando software para simulación, sistemas físicos de primer orden y segundo orden reales. Con el fin de incrementar la confianza del control propuesto, se evaluó el desempeño utilizando técnicas de control clásico, la respuesta a una entrada escalón. El control propuesto se implementó en un sensor inteligente que utiliza un microcontrolador. El desempeño del control propuesto se comparó con un control comercial. También, aquí se presenta un sensor inteligente con capacidad de control que puede mantener en control una amplia variedad de sistemas físicos. Los experimentos realizados demuestran la capacidad del control propuesto, el cual puede ser utilizado fácilmente y ahorrar tiempo en el trabajo inicial de sintonizar el control.
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Acknowledgments
This work was financially supported by the Instituto Tecnológico de Chihuahua, FOMIX-Chihuahua (Project No. CHIH-2009-C01-116741) and PROMEP (Project No. ITCH-EXB-001). Thanks are also to the students at the Instrumentation and Control Laboratory (year 2010-2011).
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