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

Autor:	Intidam, Abdessamad, El Fadil, Hassan, Housny, Halima, El Idrissi, Zakariae, Lassioui, Abdellah, Nady, Soukaina, Jabal Laafou, Abdeslam
Předmět:	BRUSHLESS electric motors MOTOR fuels PARTICLE swarm optimization FUEL cell vehicles ELECTRIC vehicle batteries PROTON exchange membrane fuel cells SPEED
Zdroj:	Energies (19961073); Jun2023, Vol. 16 Issue 11, p4395, 23p
Abstrakt:	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. [ABSTRACT FROM AUTHOR]
Databáze:	Complementary Index
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