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

Abdessamad Intidam, Hassan El Fadil (), Halima Housny, Zakariae El Idrissi, Abdellah Lassioui, Soukaina Nady and Abdeslam Jabal Laafou
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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

Energies, 2023, vol. 16, issue 11, 1-23

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.

Keywords: PEM fuel cell; brushless DC (BLDC) motor; proportional integral controller (PI); adaptive neuro-fuzzy inference system (ANFIS); particle swarm optimization (PSO); speed control; dSPACE DS1202 real-time control (RTC) card; experimental validation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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