• Journal of international Conference on Electrical Machines and Systems
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• v.1 no.2
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• pp.91-97
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• 2012

This paper presents a state space model of a two quadrant chopper and PWM inverter-fed Interior Permanent Magnet Synchronous Motor (IPMSM) drive system and its application to hybrid vehicles. The drive system has two different state equations for motoring and regenerating action. This paper presents a common state equation by using State Space Averaging method. Using this model of the IPMSM drive system, detailed simulation and controller design of the drive system, including PWM inverter switching, are given. The validity of this model and usefulness, according to a comparison among Maximum Torque/Ampere control, Maximum Torque/Flux control, and Maximum Efficiency optimization, are confirmed from simulation results.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.52 no.1
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• pp.1-8
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• 2003

The paper proposes instantaneous torque control of IPMSM for maximum torque drive of torque and current plane. The control scheme is based on the mathematical model of the motor and is applicable to the constant torque and field weakening operations. The scheme allows the motor to be driven with maximum torque per ampere(MTPA) characteristic below base speed and it maintains the maximum voltage limit of the motor wide field weakening and the motor current limit under all conditions of operation accurately. For each control mode, a condition that determines the optimal d-axis current $^id$ for maximum torque operation is derived. The proposed control algorithm is applied to IPMSM drive system for drive of wide speed range, the operating characteristics controlled that maximum torque control are examined in detail by simulation.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2005.11a
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• pp.321-326
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• 2005

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. The paper is proposed maximum torque control of IPMSM for high speed drive. 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 $^i{_d}$ for maximum torque operation is derived. The proposed control algorithm is applied to IPMSM drive system for high speed drive, the operating characteristics controlled by maximum torque control are examined in detail by experiment.

• Journal of Power Electronics
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• v.12 no.3
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• pp.468-476
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• 2012

The interior permanent magnet synchronous motor (IPMSM) has been widely used in electric vehicle applications due to its excellent power to weigh ratio. This paper proposes the maximum torque control of an IPMSM drive using an adaptive learning (AL) fuzzy neural network (FNN) and an artificial neural network (ANN). This control method is applicable over the entire speed range while taking into consideration the limits of the inverter's rated current and voltage. This maximum torque control is an executed control through an optimal d-axis current that is calculated according to the operating conditions. This paper proposes a novel technique for the high performance speed control of an IPMSM using AL-FNN and ANN. The AL-FNN is a control algorithm that is a combination of adaptive control and a FNN. This control algorithm has a powerful numerical processing capability and a high adaptability. In addition, this paper proposes the speed control of an IPMSM using an AL-FNN, the estimation of speed using an ANN and a maximum torque control using the optimal d-axis current according to the operating conditions. The proposed control algorithm is applied to an IPMSM drive system. This paper demonstrates the validity of the proposed algorithms through result analysis based on experiments under various operating conditions.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.52 no.4
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• pp.154-160
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• 2003

This paper is proposed a ANN-based rotor position and speed estimation method for IPMSM by measuring the currents. Because the proposed estimator treats the estimated motor speed as the weights, it is possible to estimate motor speed to adapt back propagation algorithm with 2 layered neural network. The proposed control algorithm is applied to IPMSM drive system. The operating characteristics controlled by neural networks are examined in detail.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.56 no.2
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• pp.74-82
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• 2007

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. This paper proposes efficiency optimization control of IPMSM drive using adaptive fuzzy learning control fuzzy neural network (AFLC-FNN) controller. In order to maximize the efficiency in such applications, this paper proposes the optimal control method of the armature current. The controllable electrical loss which consists of the copper loss and the iron loss can be minimized by the optimal control of the armature current. The minimization of loss is possible to realize efficiency optimization control for the proposed IPMSM. The optimal current can be decided according to the operating speed and the load conditions. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using AFLC-FNN controller. Also, this paper proposes speed control of IPMSM using AFLC-FNN and estimation of speed using ANN controller. 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 AFLC-FNN controller, the operating characteristics controlled by efficiency optimization control are examined in detail.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.59 no.3
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• pp.279-287
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• 2010

Interior permanent magnet synchronous motor(IPMSM) adjustable speed drives offer significant advantages over induction motor drives in a wide variety of industrial applications such as high power density, high efficiency, improved dynamic performance and reliability. This paper proposes efficiency optimization control of IPMSM drive using adaptive fuzzy learning controller(AFLC). In order to optimize the efficiency the loss minimization algorithm is developed based on motor model and operating condition. The d-axis armature current is utilized to minimize the losses of the IPMSM in a closed loop vector control environment. The design of the current based on adaptive fuzzy control using model reference and the estimation of the speed based on neural network using ANN controller. The controllable electrical loss which consists of the copper loss and the iron loss can be minimized by the optimal control of the armature current. The minimization of loss is possible to realize efficiency optimization control for the proposed IPMSM. The optimal current can be decided according to the operating speed and the load conditions. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using AFLC. Also, this paper proposes speed control of IPMSM using AFLC1, current control of AFLC2 and AFLC3, and estimation of speed using ANN controller. The proposed control algorithm is applied to IPMSM drive system controlled AFLC, the operating characteristics controlled by efficiency optimization control are examined in detail.

• The Transactions of the Korean Institute of Power Electronics
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• v.10 no.3
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• pp.282-289
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• 2005

This paper proposes a fuzzy neural network controller based on the vector control for interior permanent magnet synchronous motor(IPMSM) drive system. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability This paper does not oかy presents speed control of IPMSM using neuro-fuzzy control(NFC) but also speed estimation using artificial neural network(ANN) controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed, so that the actual state variable will coincide with the desired one. The back propagation mechanism is easy to derive and the estimated speed tracks precisely the actual motor speed. Thus, it is presented the theoretical analysis as well as the analysis results to verify the effectiveness of the proposed method in this paper.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.9
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• pp.1700-1707
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• 2011

Interior permanent magnet synchronous motor(IPMSM) adjustable speed drives offer significant advantages over induction motor drives in a wide variety of industrial applications such as high power density, high efficiency, improved dynamic performance and reliability. Since the fuzzy neural network(FNN) is recognized general approximate method to control non-linearities and uncertainties, the development of FNN control systems have also grown rapidly. The FNN controller is compounded of fuzzy and neural network. It has an advantage that is the robustness of fuzzy control and the ability to adapt of neural network. However, the FNN has static problem due to their feed-forward network structure. This paper proposes high performance speed control of IPMSM drive using the recurrent FNN(RFNN) which improved conventional FNN controller. The RFNN has excellent dynamic response characteristics because of it has internally feed-back structure. Also, this paper proposes speed estimation of IPMSM drive using ANN. The proposed method is analyzed and compared to conventional FNN controller in various operating condition such as parameter variation, steady and transient states etc.

• Proceedings of the KIPE Conference
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• 2010.07a
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• pp.57-58
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• 2010

This paper proposes maximum torque control of IPMSM drive using optimal current. This control method is applicable over the entire speed range which 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 $i_d$ for maximum torque operation is derived. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using multi-MFC and ANN controller. Also, this paper proposes maximum control of IPMSM drive using approximation method. This method is decreased the burden of digital signal process(DSP) in calculation of optimal current. This paper proposes the analysis results to verify the effectiveness of the MFC and ANN controller. Also it verifies the validity of maximum torque control of IPMSM drive with optimal current.