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

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