Published

2020-09-17

Control of a Direct Current Motor Using Time Scaling

Control de un motor de corriente continua usando escalamiento temporal

DOI:

https://doi.org/10.15446/ing.investig.v40n3.80124

Keywords:

DC motor, Time Scaling Control, plant identification, neural network, intelligent control (en)
motor DC, Control con Escalamiento Temporal, identificación de plantas, redes neuronales, control inteligente (es)

Downloads

Authors

Humans naturally control their surrounding space. However, that capacity has not been fully used to build better intelligent controllers, mainly because the reaction time of a person limits the number of industrial applications. In this paper, the author propose a method to overcome the problem of reaction time for a human in the control loop. This method, called Time Scaling Control, starts by modifying the constant times of the plant’s model to the point where control is comfortable for a human. Then, the controller acquires the knowledge that was expressed during the human control stage and places it in a Neural Network, which controls both scaled and original plants. Time Scaling Control highly improves the control performance compared with a PID, in this case demonstrated by the control of a direct current motor, which cannot be controlled by a human without time scaling control due to the speed of the system.

Los humanos controlan el espacio que los rodea de manera natural. Sin embargo, esta capacidad no se ha usado completamente para construir mejores controladores inteligentes, principalmente porque el tiempo de reacción de una persona limita el número de posibles aplicaciones industriales. En este artículo se propone un método para eliminar el problema del tiempo de reacción de un humano en un lazo de control. Este método, llamado Control con Escalamiento Temporal, comienza por modificar las constantes de tiempo del modelo de la planta, hasta el punto en el que el control sea cómodo para un humano. Entonces, el controlador adquiere el conocimiento que fue expresado durante la etapa de control humano y lo ubica en una red neuronal, la cual controla tanto la planta escalizada como la planta original. El Control con Escalamiento Temporal mejora bastante el desempeño del control en comparación con un PID, demostrado en este caso por el control de un motor de corriente directa, el cual no puede ser controlado por una persona sin el uso de escalamiento temporal por la velocidad del motor.

References

Anoop, C.S. and George, B. (2013). New Signal Conditioning Circuit for MR Angle Transducers with Full-Circle Range. IEEE Transactions on Instrumentation and Measurement, 62(5), 1308-1317. https://doi.org/10.1109/TIM.2012.2236778

Aquino, A. and Velez, M. (2006). Gray-Box Modeling of Electric Drives using Recursive Identification and Radial Basis Functions. Paper presented at IECON 2006 - 32nd Annual Conference on IEEE Industrial Electronics, Paris, 1447-1452. https://doi.org/10.1109/IECON.2006.348111

Corno, M., Giani, P., Tanelli, M., and Savaresi, S.M. (2015). Human-in-the-Loop Bicycle Control via Active Heart Rate Regulation. IEEE Transactions on Control Systems Technology, 23(3), 1029-1040. https://doi.org/10.1109/TCST.2014.2360912

Duverne, S. and Koehlin, E. (2017). Rewards and Cognitive Control in the Human Prefrontal Cortex. Cerebral Cortex, 27(1), 5024-5039. https://doi.org/10.1093/cercor/bhx210

Huang, J., Chen, Y., and Li, Z. (2015). Human operator modeling based on fractional order calculus in the manual control system with second-order controlled element. Paper presented at The 27th Chinese Control and Decision Conference (2015 CCDC), Qingdao, 4902-4906, https://doi.org/10.1109/CCDC.2015.7162802

Inga, J., Köpf, F., Flad, M., and Hohmann, S. (2017). Individual human behavior identification using an inverse reinforcement learning method. Paper presented at 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Banff, AB, 99-104. https://doi.org/10.1109/SMC.2017.8122585

Kara, T. and Eker, I. (2004). Nonlinear modeling and identification of a DC motor for bidirectional operation with real time experiments. Energy Conversion and Management, 45(1), 1087-1106. https://doi.org/10.1016/j.enconman.2003.08.005

Laurense, V.A., Pool, D.M., Damveld, H.J., Paassen, M.R.M., and Mulder, M. (2015). Effects of Controlled Element Dynamics on Human Feedforward Behavior in Ramp-Tracking Tasks. IEEE Transactions on Cybernetics, 45(2), 253-265. https://doi.org/10.1109/TCYB.2014.2324037

Lee, D. (2015). Incremental robot skill learning by human motion retargetting and physical human guidance. Paper presented at the 12th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), Goyang, 585-586. https://doi.org/10.1109/URAI.2015.7358837

Mackie, M.A., Van Dam, N.T., and Fan, J. (2013). Cognitive Control and Attentional Functions. Brain Cognitions, 82(3), 301-312. https://doi.org/10.1016/j.bandc.2013.05.004

Medaglia, J., Pasqualetti, F., Hamilton, R., Thompson, S., and Bassett, D. (2017). Brain and Cognitive Reserve: Translation via Network Control Theory. Neuroscience and Biobehavioral Reviews, 75(1), 53-64, https://10.1016/j.neubiorev.2017.01.016

Rairán, J. D. (2017). Control of Dynamic Systems Using Time Scaling. ASME 2017 Dynamic Systems and Control Conference, 1(1), 1-10. https://doi.org/10.1115/DSCC2017-5048

Rapos, D., Mechefske, C., and Timusk, M. (2016). Dynamic sensor calibration: A comparative study of a Hall Effect sensor and an incremental encoder for measuring shaft rotational position. Paper presented at 2016 IEEE International Conference on Prognostics and Health Management (ICPHM), Ottawa, ON, 1-5. https://doi.org/10.1109/ICPHM.2016.7542858

Rios, F. and Makableh, Y.F. (2011). Efficient position control of DC Servomotor using backpropagation Neural Network. Paper presented at 2011 Seventh International Conference on Natural Computation, Shanghai, 653-657. https://doi.org/10.1109/ICNC.2011.6022230

Robla, S., Becerra, V.M., Llata, J.R., González, E., Torre, C., and Pérez, J. (2017). Working Together: A Review on Safe Human-Robot Collaboration in Industrial Environments. IEEE Access, 5(1), 26754-26773. https://doi.org/10.1109/ACCESS.2017.2773127

Suresh, P. and Manivannan, P.V. (2016). Human driver emulation and cognitive decision making for autonomous cars. Paper presented at the International Conference on Robotics: Current Trends and Future Challenges (RCTFC), Thanjavur, 1-6. https://doi.org/10.1109/RCTFC.2016.7893411

Van der El, K., Pool, D.M., Van Paassen, M.R.M., and Mulder, M. (2018). Effects of Preview on Human Control Behavior in Tracking Tasks with Various Controlled Elements. IEEE Transactions on Cybernetics, 48(4), 1242-1252. https://doi.org/10.1109/TCYB.2017.2686335

Wu, S. T. and Wang, Z. L. (2016). Equilateral Measurement of Rotational Positions with Magnetic Encoders. IEEE Transactions on Instrumentation and Measurement, 65(10), 2360-2368. https://doi.org/10.1109/TIM.2016.2578579

Wahyunggoro, O. and Saad, N. (2010). Analysis and evaluation of real-time and s-domain model of A DC servomotor. Paper preseted at 2010 International Conference on Intelligent and Advanced Systems, Kuala Lumpur, Malaysia, 1-5. https://doi.org/10.1109/ICIAS.2010.5716206

Xu, K. Z., Anderson, B. A., Emeric, E. E., Sali, A. W., Stuphorn, V., Yantis, S., and Courtney, S.M. (2017). Neural Basis of Cognitive Control over Movement Inhibition: Human fMRI and Primate Electrophysiology Evidence. Neuron, 96(6), 1447-1458. https://doi.org/10.1016/j.neuron.2017.11.010

Dimensions

PlumX

Article abstract page views

1023

Downloads

Download data is not yet available.

How to Cite

Control of a Direct Current Motor Using Time Scaling. (2020). Ingeniería E Investigación, 40(3), 38-46. https://doi.org/10.15446/ing.investig.v40n3.80124