Simulation and modelling of a variable gain PI controller for speed control of a direct torque neuro fuzzy controlled induction machine drive

Abstract

This paper presents an original variable gain PI (VGPI) controller for speed control of a direct torque neuro fuzzy controlled (DTNFC) induction motor drive. First, a VGPI speed controller is designed to replace the classical PI controller in a conventional direct torque controlled induction motor drive. Its simulated performances are then compared to those of a classical PI controller. Then, a direct torque neuro fuzzy control (DTNFC) for a voltage source PWM inverter fed induction motor drive is presented. This control scheme uses the stator flux amplitude and the electromagnetic torque errors through an adaptive NF inference system (ANFIS) to generate a voltage space vector (reference voltage) which is used by a space vector modulator to generate the inverter switching states. In this paper a new ANFIS structure is proposed. This structure generates the desired reference voltage by acting on both the amplitude and the angle of its components. Simulation of the DTNFC induction motor drive using VGPI for speed control shows promising results. The motor reaches the reference speed rapidly and without overshoot, load disturbances are rapidly rejected and variations of some of the motor parameters are fairly well dealt with.