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Performance evaluation of a Time Scale Controller
Evaluación del rendimiento de un controlador basado en escalamiento temporal
DOI:
https://doi.org/10.15446/dyna.v89n220.96502Palabras clave:
time scaling control, neural networks, fuzzy control, intelligent control, position control (en)control por escalamiento temporal, redes neuronales, controlador difuso, controlador inteligente, control de posición (es)
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Regardless of the advances in intelligent control the analysis and use of the human capacity to control are far from exhausted. For instance, industrial applications could be too fast or too slow for a human to control. The proposed solution in this paper starts by scaling the model of the system in time, so that it results comfortable to control. The control actions of the human are learned by a Neural Network, which is blind to the changes in the time scale, thus the Neural Network controls the scaled model and the real plant as well. The Time Scaling Controller is evaluated by controlling the angular position of a motor and the result is contrasted with a fuzzy controller and a piecewise linear controller. Time Scaling Control resulted better than the other two controllers because it has the lowest effort and the highest effectiveness among the three controllers.
Aun con todo el avance en el control inteligente, el análisis y el uso de la capacidad humana para controlar está lejos de haber terminado. Por ejemplo, las aplicaciones industriales pueden ser muy rápidas o lentas para que una persona las controle. La solución propuesta en este artículo comienza con el escalamiento temporal de un sistema, hasta que este resulte cómodo de controlar. La acción de control humana es aprendida por una red neuronal, la cual es ciega a los cambios en el tiempo, así, la red neuronal controla tanto el modelo escalizado como la planta real. Se prueba un controlador basado en escalamiento temporal por medio del control de la posición angular de un motor, el resultado es contrastado con un controlador difuso y con un controlador lineal a trozos. El control con escalamiento temporal es mejor que los otros dos controladores porque utiliza el menor esfuerzo posible y a la vez presenta la mejor efectividad.
Referencias
Santoso, F., Garratt, M.A. and Anavatti, S.G., State-of-the-art intelligent flight control systems in unmanned aerial vehicles, in: IEEE Transactions on Automation Science and Engineering, 15(2), pp. 613-627, 2018. DOI: https://doi.org/10.1109/TASE.2017.2651109
Wang, D., He, H. and Liu, D., Intelligent optimal control with critic learning for a nonlinear overhead crane system, in: IEEE Transactions on Industrial Informatics, 14(7), pp. 2932-2940, 2018. DOI:https://doi.org/10.1109/TII.2017.2771256
dos Santos, J.D.B. and Bessa, W.M., Intelligent control for accurate position tracking of electrohydraulic actuators, Electronics Letters,55(2), pp. 78-80, 2019. DOI: https://doi.org/10.1049/el.2018.7218
Harrabi, N., Souissi, M., Aitouche, A. and Chaabane, M., Intelligent control of grid-connected AC–DC–AC converters for a WECS based on T–S fuzzy interconnected systems modelling, IET Power Electronics, 11(9), pp. 1507-1518, 2018. DOI: https://doi.org/10.1049/iet-pel.2017.0174
Chen, P., Han, Y., Zhang, P. and Jiang, Y., Intelligent S-Plane controller for micro unmanned aerial vehicle, IEEE Access, 6(1), pp. 68096-68103, 2018. DOI: https://doi.org/10.1109/ACCESS.2018.2879063
Gheisarnejad, M. and Khooban, M., Supervised control strategy in trajectory tracking for a wheeled mobile robot, in IET Collaborative Intelligent Manufacturing, 1(1), pp. 3-9, 2019. DOI:https://doi.org/10.1049/iet-cim.2018.0007
El-Hussieny, H., Abouelsoud, A.A., Assal, F.M. and Megahed, S.M., Adaptive learning of human motor behaviors: an evolving inverse optimal control approach, Engineering Applications of Artificial Intelligence, 50(1), pp. 115-124, 2016. DOI: https://doi.org/10.1016/j.engappai.2016.01.024
Zhang, X. and Hoagg, J.B., Frequency-domain subsystem identification with application to modeling human control behavior, Systems & Control Letters, 87(1), pp. 36-46, 2016. DOI: https://doi.org/10.1016/j.sysconle.2015.10.009
Inga, J., Eitel, M., Flad, M. and Hohmann, S., Evaluating human behavior in manual and shared control via inverse optimization, in: 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Miyazaki, Japan, 2018, pp. 2699-2704. DOI: https://doi.org/10.1109/SMC.2018.00461
Maurice, P., Huber, M.E., Hogan N. and Sternad, D., Velocitycurvature patterns limit human–robot physical interaction, in: IEEE Robotics and Automation Letters, 3(1), pp. 249-256, 2018. DOI: https://doi.org/10.1109/LRA.2017.2737048
Park, S., Marino, H., Charles, S., Sternad, D. and Hogan, N., Moving slowly is hard for humans: limitations of dynamic primitives, J Neurophysiol., 118(1), pp. 69-83, 2017. DOI: https://doi.org/10.1152/jn.00643.2016.
van der El, K., Pool, D.M., van Paassen, M.R.M. and Mulder, M.,Effects of preview on human control behavior in tracking tasks with various controlled elements, IEEE Transactions on Cybernetics, 48(4),pp. 1242-1252, 2018. DOI: https://doi.org/10.1109/TCYB.2017.2686335
Chang, Y., Architecture design for performing grasp-and-lift tasks in brain–machine-interface-based human-in-the-loop robotic system, IET Cyber-Physical Systems: Theory & Applications, 4(3), pp. 198-203, 2019. DOI: https://doi.org/10.1049/iet-cps.2018.5066
Inoue, M. and Gupta, V., “Weak” control for human-in-the-loop systems, IEEE Control Systems Letters, 3(2), pp. 440-445, 2019. DOI: https://doi.org/10.1109/LCSYS.2019.2891489
Bae, S., Han, S.M. and Moura, S., Modeling & control of human actuated systems, IFAC-PapersOnLine, 51(34), pp. 40-46, 2019. DOI: https://doi.org/10.1016/j.ifacol.2019.01.016
Verma, A. and Mettler, B., Analysis of human interaction patterns emerging while learning agile navigation of unknown environments, IFAC-PapersOnLine, 51(34), pp. 75-82, 2019, DOI: https://doi.org/10.1016/j.ifacol.2019.01.031
Joos, A., Hoffmann, M. and Stein, T., Human center of mass trajectory models using nonlinear model predictive control, IFACPapersOnLine, 51(22), pp. 366-371, 2018. DOI: https://doi.org/10.1016/j.ifacol.2018.11.569
Rairan, J.D., Control of dynamic systems using time scaling. in:ASME 2017 Dynamic Systems and Control Conference. 1(1), pp. 1-10, Tysons, Virginia, USA, October 11-13, 2017. DOI:https://doi.org/10.1115/DSCC2017-5048
Optican, L.M. and Pretegiani, E., Chapter 1 - Mathematical models and human disease, in: Ramat, S. and Shaikh, A.G., (Eds), Progress in Brain Research, Elsevier, Vol. 248, 2019, pp. 3-18. DOI: https://doi.org/10.1016/bs.pbr.2019.02.010
Nguyen, A.T., Taniguchi, T., Eciolaza, L., Campos, V., Palhares, R. and Sugeno. M., Fuzzy control systems: past, present and future, IEEE Computational Intelligence Magazine, 14(1), pp. 56-68, 2019. DOI:https://doi.org/10.1109/MCI.2018.2881644
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