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Development and Experimental Implementation of Optimized PI-ANFIS Controller for Speed Control of a Brushless DC Motor in Fuel Cell Electric Vehicles

Author

Listed:
  • Abdessamad Intidam

    (ISA Laboratory, ENSA, Ibn Tofail University, Kenitra 14000, Morocco)

  • Hassan El Fadil

    (ISA Laboratory, ENSA, Ibn Tofail University, Kenitra 14000, Morocco)

  • Halima Housny

    (ISA Laboratory, ENSA, Ibn Tofail University, Kenitra 14000, Morocco)

  • Zakariae El Idrissi

    (ISA Laboratory, ENSA, Ibn Tofail University, Kenitra 14000, Morocco)

  • Abdellah Lassioui

    (ISA Laboratory, ENSA, Ibn Tofail University, Kenitra 14000, Morocco)

  • Soukaina Nady

    (ISA Laboratory, ENSA, Ibn Tofail University, Kenitra 14000, Morocco)

  • Abdeslam Jabal Laafou

    (ISA Laboratory, ENSA, Ibn Tofail University, Kenitra 14000, Morocco)

Abstract

This paper compares the performance of different control techniques applied to a high-performance brushless DC (BLDC) motor. The first controller is a classical proportional integral (PI) controller. In contrast, the second one is based on adaptive neuro-fuzzy inference systems (proportional integral-adaptive neuro-fuzzy inference system (PI-ANFIS) and particle swarm optimization-proportional integral-adaptive neuro-fuzzy inference system (PSO-PI-ANFIS)). The control objective is to regulate the rotor speed to its desired reference value in the presence of load torque disturbance and parameter variations. The proposed controller uses a dSPACE platform (MicroLabBox controller board). The experimental prototype comprises a PEMFC system (the Nexa Ballard FC power generator: 1.2 kW, 52 A) and a brushless DC motor BLDC of 1 kW 1000 rpm. The PSO-PI-ANFIS controller presents better performance than the PI-ANFIS and classical PI controllers due to its ability to optimize the PI-ANFIS controller’s parameters using the particle swarm optimization (PSO) algorithm. This optimization results in improved tracking accuracy and reduced overshoot and settling time.

Suggested Citation

  • Abdessamad Intidam & Hassan El Fadil & Halima Housny & Zakariae El Idrissi & Abdellah Lassioui & Soukaina Nady & Abdeslam Jabal Laafou, 2023. "Development and Experimental Implementation of Optimized PI-ANFIS Controller for Speed Control of a Brushless DC Motor in Fuel Cell Electric Vehicles," Energies, MDPI, vol. 16(11), pages 1-23, May.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:11:p:4395-:d:1158952
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    References listed on IDEAS

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    1. Mohamed Dahbi & Said Doubabi & Ahmed Rachid, 2018. "Current Spikes Minimization Method for Three-Phase Permanent Magnet Brushless DC Motor with Real-Time Implementation," Energies, MDPI, vol. 11(11), pages 1-14, November.
    2. Giuseppe De Lorenzo & Francesco Piraino & Francesco Longo & Giovanni Tinè & Valeria Boscaino & Nicola Panzavecchia & Massimo Caccia & Petronilla Fragiacomo, 2022. "Modelling and Performance Analysis of an Autonomous Marine Vehicle Powered by a Fuel Cell Hybrid Powertrain," Energies, MDPI, vol. 15(19), pages 1-21, September.
    3. Muhammad Asyraf Azni & Rasyikah Md Khalid & Umi Azmah Hasran & Siti Kartom Kamarudin, 2023. "Review of the Effects of Fossil Fuels and the Need for a Hydrogen Fuel Cell Policy in Malaysia," Sustainability, MDPI, vol. 15(5), pages 1-16, February.
    4. Shukri Mahmood Younus Younus & Uğurhan Kutbay & Javad Rahebi & Fırat Hardalaç, 2023. "Hybrid Gray Wolf Optimization–Proportional Integral Based Speed Controllers for Brush-Less DC Motor," Energies, MDPI, vol. 16(4), pages 1-18, February.
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