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).

 

References

[1] . Frank R., In Understanding Smart Sensors; Artech House: Norwood, Massachusetts, 2000, pp. 3.         [ Links ]

[2]. Robert M., Riviere J. M., Noizette J. L.; Hermann, F., Smart sensors in flexible manufacturing systems, Sensors and Actuators: A Physical, Vol. 37-38, No. C, June, 1993, pp. 239-246.         [ Links ]

[3]. Meijer, G. C. M., Concepts and focus point for intelligent sensor systems Sensors and Actuators: A Physical. Vol 41, No. 1-3, April, 1994, pp. 183-191.         [ Links ]

[4]. Tian, G. Y., Zhao Z. X., Baines, R. W., Fieldbus-based intelligent sensor, Mechatronics, Vol. 10, No. 8, December, 2000, pp 835-849.         [ Links ]

[5]. Mekid S., Further Structural Intelligence for Sensors Cluster Technology in Manufacturing. Sensors, No. 6, June, 2006, pp. 557-577.         [ Links ]

[6]. Rivera J., Herrera G., Chacón M., Acosta P., Carrillo M., Improved Progressive Polynomial Algorithm for Self-Adjustment and Optimal Response in Intelligent Sensors, Sensors, No. 8, November, 2008, pp. 7410-7427.         [ Links ]

[7]. Hernandez W., Robust Multivariable Estimation of the Relevant Information Coming from a Wheel Speed Sensor and an Accelerometer Embedded in a Car under Performance Test. Sensors, No. 5, November, 2005, pp. 488-508.         [ Links ]

[8]. Hernandez. W., Improving the Response of a Load Cell by Using Optimal Filtering. Sensors, No. 6, July, 2006, pp. 697-711.         [ Links ]

[9]. Miskowiccz, M., Asymptotic Effectiveness of the Event-Based Sampling according to the Integral Criterion. Sensors, No. 7, January 2007, pp. 16-37.         [ Links ]

[10]. Hernandez, W., A Survey on Optimal Signal Processing Techniques Applied to Improve the Performance of Mechanical Sensors in Automotive Applications. Sensors, No. 7, January 2007, pp. 84-102.         [ Links ]

[11] . Hernandez W., De Vicente J., Sergiyenko O.Y., Fernández E., Improving the Response of Accelerometers for Automotive Applications by Using LMS Adaptive Filters. Sensors, No. 10, December, 2010, pp. 313-329.         [ Links ]

[12]. Hernandez W., De Vicente J., Sergiyenko O.Y., Fernández E., Improving the Response of Accelerometers for Automotive Applications by Using LMS Adaptive Filters: Part II. Sensors No. 10, January, 2010, pp. 952-962.         [ Links ]

[13]. Heong K., Chong G., PID Control System Analysis, Design, and Technology. IEEE Transactions on Control Systems Technology. Vol. 13, No.4, July, 2005, pp. 559-576.         [ Links ]

[14]. Palma L.S., Oliveira A., Costa A. S., Andrade A. Q., Almeida C. V. R., Zurita M. E. P. V., Freire R. C. S. Implementation of a I2-Controlled Constant Temperature Environment Temperature Meter, Sensors No. 3, October, 2003, pp. 498-503.         [ Links ]

[15]. Sanchéz J., Guarnes M. A., Dormido S. On the Aplication of Diferent Event-Based Sampling Strategies to the Control of a Simple Industrial Process, Sensor, No. 9, August 2009, pp. 6795-6818.         [ Links ]

[16]. Treeprapin K., Kanzaki A., Hara T., Nishio S. An Effective Mobile Sensor Control Method for Sparse Sensor Networks. Sensors, No. 9, January 2009, pp. 327-354.         [ Links ]

[17]. Xia F., Ma L., Peng Ch., Sun Y., Dong J. Cross-Layer Adaptive Scheduling of Wireless Control Systems. Sensors, No. 8, July, 2008, pp. 4265-4281.         [ Links ]

[18]. Gutiérrez A., Jiménez L. J., Magdalena L. A Distributed Sensor Network for the Control of a Bioclimatic House in Spain. Sensors, No. 9, October 2009, pp. 8197-8214.         [ Links ]

[19]. Wu M., Cao W., He Ch. Y., She J.H. Integrated Intelligent of Gas Mixing and Pressurization Process. IEEE Transactions on Control Systems Technology. Vol. 17, No.1, January, 2009, pp. 68-77.         [ Links ]

[20]. Xi F., Zhao W., Sun Y., Tian Y. Ch., Fuzzy Logic Control Based QoS Management in Wireless Sensor/Actuator Network. Sensors, No. 7, December, 2007, pp 3179-3191.         [ Links ]

[21]. Jung S., Kim S. S., Control Experiment of Wheel-Driven Mobile Inverted Pendulum Using Neural Network. IEEE Transactions on Control Systems Technology. Vol.16, No.2, March, 2008, pp 297-303.         [ Links ]

[22]. Wang J. J., Zhang C. F., Jing Y.Y., Self-Adaptive RBF Neural Network PID Control in Exhaust Temperature of Micro Gas Turbine. Proceeding of 7TH International Conference on Machine Learning and Cybernetics, 2008, pp. 2131-2135, Kunming, China, July.         [ Links ]

[23]. Han W. Y., Han J. W., Lee, Development of Self-tuning PID Controller base on Neural Network for Nonlinear Systems. Proceedings of the 7th Mediterranean on Control and Automation, 1999, pp 979988, Haifa Israel, June.         [ Links ]

[24]. Chang W. D., Hwang R.Ch., Hsieh J.G., A multivariable on-line adaptive PID controller using auto-tuning neurons. Engineering Applications of Artificial Intelligence, Vol. 16, No.1, February, 2003, pp. 57-63.         [ Links ]

[25]. Mohamadian M., Nowicki E.P., Salmon J.C., A neural Network Controller for Indirect Field Orientation Control, Conference Record of the 1995 IEEE Industrial Applications Conference Thirtieth IAS Annual Meeting. 1995, pp. 1770-1774, Orlando, United States of America, October.         [ Links ]

[26]. Lizarraga I., Etxebarria V., Real-time adaptive neural control of class of nonlinear systems Engineering Applications of Artificial Intelligence, Vol. 12, No.1, February, 1999, pp. 3-19.         [ Links ]

[27]. Zerkaoui S., Druaux F., Leclercq E. Lefebvre D., Stable adaptive control with neural networks for square MIMO no-linear systems. Engineering Applications of Artificial Intelligence, Vol. 22, No. 4-5, June, 2009, pp. 702-717.         [ Links ]

[28]. Peng H., Yang Z. J., Gui W., Wu M., Shioya H., Nakano K., Nonlinear System Modeling and robust predictive control based on RBF-ARX model. Engineering Applications of Artificial Intelligence, Vol. 20, No. 1, February, 2007, pp 1-9.         [ Links ]

[29]. Jung S., Kim S. S., Hardware Implementation or Real-Time Neural Network Controller With a DSP and FPGA for Nonlinear Systems. IEEE Transactions on Industrial Electronics. Vol. 54, No.1, February, 2007, pp. 265-271.         [ Links ]

[30]. Ning W., Shuqing W., Jianming Z. A Model Reference Intelligent Controller Using a Single Neuron. Proceeding of International Conference On Info-tech. and Info-net 2001, ICII 2001, 2001, pp. 170-173, Beijing, China, October.         [ Links ]

[31]. Sheng Q., Xianyi Z., Changhong W., Gao X. Z., Zilong L. Design and Implementation of An Adaptive PID Controller Using Single Neuron Learning Algorithm. Proceeding of the 4th World On Intelligent Control and Automation, 2002, pp. 2279-2283. Shanghai, China, June.         [ Links ]

[32]. Qin Z. M., Zhang L. Y., Gu J. J., Wang L., Single_Neuron PSD Adaptive Control Based on Smith Predictor for the Main Stream Temperature. Proceeding of the Third International Conference on Machine Learning and Cybernetics. 2004, pp. 1088-1092, Changhai, China, August.         [ Links ]

[33]. Sun D., Meng J., A Single Neuron PID Controller Based PMSM DTC Drive System Fed by Fault Tolerant 4-Switch 3-Phase Inverter, 1st IEEE Conference on PID Based on a Single Artificial Neural Network Algorithm for Intelligent Sensors, J. Rivera-Mejía et al. / 262-282 Industrial Electronics and Applications, 2006, pp. 1-5, Singapore, Singapore, May.         [ Links ]

[34]. Shi T., Xia Ch., Wang M., Zhang Q., Single Neural PID Control for Sensorless Switched Reluctance Motor Based on RBF Neural Network. Proceeding of the 6th World Congress on Intelligent Control and Automation. 2006, pp. 8069-8073, Dalian, China, June.         [ Links ]

[35]. Guang Z., Min Q. K., Yan M. S., Qi G. F., Single Neutral Element Self-Adaptive PID Controller Used In SVC. CES / IEEE 5th International Power Electronics and Motion Control Conference, IPEMC 2006, 2006, pp. 1-5, Shanghai, China, August.         [ Links ]

[36]. Zhang M. G., Li W. H., Single Neuron PID Model Reference Adaptive Control Based on RBF Neural Network. Proceeding of the Fifth International Conference On Machine Learning and Cybernetics. 2006, pp. 3021-3025, Dalian, China, August.         [ Links ]

[37]. Sun J., Chan S., Ko K. T., Chen G.; Zukerman M. Neuron PID: A Robust AQM Scheme. Proceeding of Australian Telecommunication Network and Applications Conference ATNAC 2006, 2006, pp. 259-262, Melbourne, Australia, December.         [ Links ]

[38]. Xia Ch., Li Z., Song P., Wang Y., A Current Control Algorithm based On Variable Current Threshold for Four-Switch Three-phase BLDCM using Intelligent Controller. IEEE International Conference On Industrial Technology, ICIT-2008, pp. 1-5, Chengdu, China April.         [ Links ]

[39]. Yang Z., Zhao J., Zheng T. Q., High Performance Vector Control of Linear Induction Motors Using Single Neuron controller. Fourth International Conference on Natural Computation, ICNC'08, 2008, pp. 534-537, Jinan, China, October.         [ Links ]

[40]. Zhao Q., Yang Y., Improved Single Neuron PID Control for Heavy-Duty Vehicle Magnetorheological Seat Suspension, IEEE Vehicle Power and Propulsion Conference, VPPC-2008, pp. 1-3, Harbin, China, September.         [ Links ]

[41]. Liu X., Single Neuron Self-tuning PID Control for Welding Molten Pool Depth. Proceeding of the 7th World Congress On Intelligent Control and Automation. 2008, pp. 7922-7925, Chongqing, China, June.         [ Links ]

[42]. An S., Shao L., Diesel Engine Common Rail Pressure Control Based on Neuron Adaptive PID. IEEE International Conference on Cybernetics and Intelligent Systems. 2008, pp. 714-717, Chengdu, China, September.         [ Links ]

[43]. Wang X., Xu B., Ding L., Simulation Study on A Single Neuron PID Control System of DC/DC Converters. Work Shop On Power Electronics and Intelligent Transportation System, PEITS'2008. Pp. 127-130, Guangzhou, China, August.         [ Links ]

[44]. Ogata K. Discrete-Time Control Systems, Prentice Hall Inc, 1996.         [ Links ]

[45]. Rashid H. M., Power Electronics Handbook; Academic Press: San Diego, California, 2001, pp. 169.         [ Links ]