Maximum Torque Control of IPMSM Drive with LM-FNN Controller

Interior permanent magnet synchronous motor (IPMSM) has become a popular choice in electric vehicle and servo drive applications due to their excellent power to weight ratio. The paper is proposed maximum torque control of IPMSM drive using learning mechanism-fuzzy neural network (LM-FNN) and artificial neural network (ANN). The control method is applicable over the entire speed range and considered the limits of the inverter´s current and voltage rated value. For each control mode, a condition that determines the optimal d-axis current for maximum torque operation is derived. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using LM-FNN and ANN. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also, this paper is proposed speed control of IPMSM using LM-FNN and estimation of speed using ANN. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The proposed control algorithm is applied to IPMSM drive system controlled LM-FNN and ANN, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper is proposed the experimental results to verify the effectiveness of the LM-FNN and ANN