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Optimal Fuzzy Logic Controller for DC Motor Using Grasshopper Optimization Algorithm

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New Directions on Hybrid Intelligent Systems Based on Neural Networks, Fuzzy Logic, and Optimization Algorithms

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1146))

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Abstract

Actually, technological advances in the electric motor industry have been significant, however, demand is expected to increase in the next years because the increase in the manufacture of work and household tools such as smart vacuum cleaners, and to work they need direct current (DC), without forgetting the manufacture of electric or hybrid automobiles, drones, explorer or domestic robots, et al. A high demand for this type of product is projected, because the trend and social needs are focused on autonomy, this due to the shortage of mineral resources for the production of fossil fuels for combustion in the case of the automotive industry. Thinking about that necessity, in this work a model based on intelligent computation is presented to build and determine an optimal controller based on fuzzy logic to take advantage of the maximum performance in motors that use DC, to evaluate, optimize and determine the optimal controller. The optimization algorithm bio-inspired by the behavior of grasshoppers in nature is used in this work and the contribution is the implementation of the grasshopper algorithm to optimize a Mamdani type fuzzy control applied to a benchmark problem with the aim of testing the performance and stability in the problem resolutions.

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References

  1. E. Agamloh, A. von Jouanne, A. Yokochi, An overview of electric machine trends in modern electric vehicles. Machines 8(2) (2020). https://doi.org/10.3390/MACHINES8020020

  2. C. Huang, F. Lei, X. Han, Z. Zhang, Determination of modeling parameters for a brushless DC motor that satisfies the power performance of an electric vehicle. Meas. Control (U.K.) 52(7–8) (2019). https://doi.org/10.1177/0020294019842607

  3. Y. Meraihi, A.B. Gabis, S. Mirjalili, A. Ramdane-Cherif, Grasshopper optimization algorithm: theory, variants, and applications. IEEE Access 9 (2021). https://doi.org/10.1109/ACCESS.2021.3067597

  4. C. Chen, T. Zhang, A review of design and fabrication of the bionic flapping wing micro air vehicles. Micromachines 10(2) (2019). https://doi.org/10.3390/mi10020144

  5. J.R. Ruiz-Sarmiento, C. Galindo, J. Gonzalez-Jimenez, Robot@Home, a robotic dataset for semantic mapping of home environments. Int. J. Rob. Res. 36(2) (2017). https://doi.org/10.1177/0278364917695640

  6. J. Sundin, K. Kokmanian, M.K. Fu, S. Bagheri, M. Hultmark, A soft material flow sensor for micro air vehicles. Soft Robot. 8(2) (2021). https://doi.org/10.1089/soro.2019.0130

  7. A. Mueller, Modern robotics: mechanics, planning, and control [bookshelf]. IEEE Control Syst. 39(6) (2022). https://doi.org/10.1109/mcs.2019.2937265

  8. M.J. Mahmoodabadi, N. Rezaee Babak, Fuzzy adaptive robust proportional–integral–derivative control optimized by the multi-objective grasshopper optimization algorithm for a nonlinear quadrotor. JVC/J. Vib. Control 26(17–18) (2020). https://doi.org/10.1177/1077546319901019

  9. S. Dwivedi, Detecting anonymous attacks in wireless communication medium using adaptive grasshopper optimization algorithm. Cogn. Syst. Res. 69 (2021). https://doi.org/10.1016/j.cogsys.2021.04.003

  10. J. Baranowski, T. Drabek, P. PiÄ…tek, A. Tutaj, Diagnosis and mitigation of electromagnetic interference generated by a brushless dc motor drive of an electric torque tool. Energies 14(8) (2021). https://doi.org/10.3390/en14082149

  11. F. Naseri, E. Farjah, E. Schaltz, K. Lu, N. Tashakor, Predictive control of low-cost three-phase four-switch inverter-fed drives for brushless DC motor applications. IEEE Trans. Circuits Syst. I Regul. Pap. 68(3) (2021). https://doi.org/10.1109/TCSI.2020.3043468

  12. S. Hassan, M.A. Khanesar, N.K. Hussein, S.B. Belhaouari, U. Amjad, W.K. Mashwani, Optimization of interval type-2 fuzzy logic system using grasshopper optimization algorithm. Comput. Mater. Contin. 71(2) (2022). https://doi.org/10.32604/cmc.2022.022018

  13. C.E.M. Barbosa, G.C. Vasconcelos, Cuckoo search optimization for short term wind energy forecasting (2016). https://doi.org/10.1109/CEC.2016.7744002

  14. A. Kaveh, M.R. Moghaddam, A hybrid WOA-CBO algorithm for construction site layout planning problem. Sci. Iran. 25(3A) (2018). https://doi.org/10.24200/sci.2017.4212

  15. L. Abualigah, M. Shehab, M. Alshinwan, S. Mirjalili, M.A. Elaziz, Ant lion optimizer: a comprehensive survey of its variants and applications. Arch. Comput. Methods Eng. 28(3) (2021). https://doi.org/10.1007/s11831-020-09420-6

  16. M.H. Nadimi-Shahraki, S. Taghian, S. Mirjalili, An improved grey wolf optimizer for solving engineering problems. Expert Syst. Appl. 166 (2021). https://doi.org/10.1016/j.eswa.2020.113917

  17. Tehzeeb-ul-Hassan, T. Alquthami, S.E. Butt, M.F. Tahir, K. Mehmood, Short-term optimal scheduling of hydro-thermal power plants using artificial bee colony algorithm. Energy Rep. 6 (2020). https://doi.org/10.1016/j.egyr.2020.04.003

  18. C. Ning, F. You, Optimization under uncertainty in the era of big data and deep learning: when machine learning meets mathematical programming. Comput. Chem. Eng. 125 (2019). https://doi.org/10.1016/j.compchemeng.2019.03.034

  19. Y. Ma, R. Han, W. Wang, Portfolio optimization with return prediction using deep learning and machine learning. Expert Syst. Appl. 165 (2021). https://doi.org/10.1016/j.eswa.2020.113973

  20. S. Latreche, S. Benaggoune, Robust wheel slip for vehicle anti-lock braking system with fuzzy sliding mode controller (FSMC). Eng. Technol. Appl. Sci. Res. 10(5) (2020). https://doi.org/10.48084/etasr.3830

  21. W. Batayneh, A. Bataineh, M.A. Jaradat, Intelligent adaptive fuzzy logic genetic algorithm controller for anti-lock braking system. Int. Rev. Model. Simul. 14(1) (2021). https://doi.org/10.15866/iremos.v14i1.19838

  22. F. Azmi, J. Louise, Z.R. Sitompul, S. Kumar, J. Surya, Design of smart garden sprinklers based on fuzzy logic. J. Inform. Telecommun. Eng. 4(1) (2020). https://doi.org/10.31289/jite.v4i1.3886

  23. A. Gozuoglu, O. Ozgonenel, S. Karagol, Fuzzy logic based low cost smart home application (2019). https://doi.org/10.23919/ELECO47770.2019.8990409

  24. V. Korolyov, M. Ogurtsov, A. Khodzinsky, Statement of the problem of complete set of UAV group on the basis of models of granular calculations and fuzzy logic. Cybern. Comput. Technol. (2) (2021). https://doi.org/10.34229/2707-451x.21.2.3

  25. A. Yohandrik Bi Dawe, P. Dani Prasetyo Adi, Electricity monitoring system based on fuzzy logic and Internet of things. Nu 01 (2021)

    Google Scholar 

  26. M.K. Hasan, M.Z. Chowdhury, M. Shahjalal, Y.M. Jang, Fuzzy based network assignment and link-switching analysis in hybrid OCC/LiFi system. Wirel. Commun. Mob. Comput. 2018 (2018). https://doi.org/10.1155/2018/2870518

  27. E. Bernal, O. Castillo, J. Soria, F. Valdez, Imperialist competitive algorithm with dynamic parameter adaptation using fuzzy logic applied to the optimization of mathematical functions. Algorithms 10(1) (2017). https://doi.org/10.3390/a10010018

  28. M. Abdel-Basset, R. Mohamed, M. Abouhawwash, Balanced multi-objective optimization algorithm using improvement based reference points approach. Swarm Evol. Comput. 60 (2021). https://doi.org/10.1016/j.swevo.2020.100791

  29. L. Yang, A. Shami, On hyperparameter optimization of machine learning algorithms: theory and practice. Neurocomputing 415 (2020). https://doi.org/10.1016/j.neucom.2020.07.061

  30. S. Biswas, P.K. Roy, K. Chatterjee, FACTS-based 3DOF-PID controller for LFC of renewable power system under deregulation using GOA. IETE J. Res. (2021). https://doi.org/10.1080/03772063.2020.1870874

    Article  Google Scholar 

  31. H. Moayedi, A. Mosavi, Double-target based neural networks in predicting energy consumption in residential buildings. Energies 14(5) (2021). https://doi.org/10.3390/en14051331

  32. S. Saremi, S. Mirjalili, A. Lewis, Grasshopper optimisation algorithm: theory and application. Adv. Eng. Softw. 105 (2017)

    Google Scholar 

  33. Seyedali Mirjalili, SEYEDALI MIRJALILI 2019 (2022), https://seyedalimirjalili.com/goa

  34. A. Tabak, Fractional order frequency proportional-integral-derivative control of microgrid consisting of renewable energy sources based on multi-objective grasshopper optimization algorithm. Trans. Inst. Meas. Control 44(2) (2022). https://doi.org/10.1177/01423312211034660

  35. A.A.S. Bahdad, S.F.S. Fadzil, H.O. Onubi, S.A. BenLasod, Sensitivity analysis linked to multi-objective optimization for adjustments of light-shelves design parameters in response to visual comfort and thermal energy performance. J. Build. Eng. 44 (2021). https://doi.org/10.1016/j.jobe.2021.102996

  36. S.J. Simpson, A.R. McCaffery, B.F. HÄgele, A behavioural analysis of phase change in the desert locust. Biol. Rev. 74(4) (1999). https://doi.org/10.1111/j.1469-185X.1999.tb00038.x

  37. S. Sadi, DC motor speed control using Mamdani fuzzy logic based on microcontroller. J. Tek. 9(2) (2020). https://doi.org/10.31000/jt.v9i2.3676

  38. Y.A. Almatheel, A. Abdelrahman, Speed control of DC motor using fuzzy logic controller (2017). https://doi.org/10.1109/ICCCCEE.2017.7867673

  39. F. Valdez, J.C. Vazquez, P. Melin, O. Castillo, Comparative study of the use of fuzzy logic in improving particle swarm optimization variants for mathematical functions using co-evolution. Appl. Soft Comput. 52, 1070–1083 (2017)

    Article  Google Scholar 

  40. O. Castillo, E. Lizzarraga, J. Soria, P. Melin, F. Valdez, New approach using ant colony optimization with ant set partition for fuzzy control design applied to the ball and beam system. Inf. Sci. 294, 203–215 (2015)

    Article  MathSciNet  Google Scholar 

  41. L. Amador-Angulo, O. Mendoza, J.R. Castro, A. Rodriguez-Diaz, P. Melin, O. Castillo, Fuzzy sets in dynamic adaptation of parameters of a bee colony optimization for controlling the trajectory of an autonomous mobile robot. Sensors 16(9), 1458 (2016)

    Article  Google Scholar 

  42. D. Sanchez, P. Melin, O. Castillo, A grey wolf optimizer for modular granular neural networks for human recognition. Comput. Intell. Neurosci. 2017 (2017). https://doi.org/10.1155/2017/4180510

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Authors and Affiliations

  1. Autonomous University of Baja California, Tijuana, Baja California, Mexico

    Camilo Caraveo, Leticia Cervantes & Jesús Soto

  2. Tijuana Institute of Technology, Tijuana, Baja California, Mexico

    Oscar Castillo

Authors
  1. Camilo Caraveo
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  2. Leticia Cervantes
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  3. Jesús Soto
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  4. Oscar Castillo
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Corresponding author

Correspondence to Leticia Cervantes .

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Editors and Affiliations

  1. Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Mexico

    Patricia Melin

  2. Division of Graduate Studies &Research, Tijuana Institute of Technology, Tijuana, Mexico

    Oscar Castillo

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Caraveo, C., Cervantes, L., Soto, J., Castillo, O. (2024). Optimal Fuzzy Logic Controller for DC Motor Using Grasshopper Optimization Algorithm. In: Melin, P., Castillo, O. (eds) New Directions on Hybrid Intelligent Systems Based on Neural Networks, Fuzzy Logic, and Optimization Algorithms. Studies in Computational Intelligence, vol 1146. Springer, Cham. https://doi.org/10.1007/978-3-031-53713-4_14

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