• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.26 no.12
• /
• pp.9-20
• /
• 2012

This paper proposes the efficiency optimization control of interior permanent magnet synchronous motor(IPMSM) drive using series connected-fuzzy neural network PI(SC-FNPI) controller. The PI controller is generally used to control IPMSM drive in industrial field. However, the PI controller has problem which is falling control performance about parameter variation such as command speed, load torque and inertia due to fixed gain of PI controller. Therefore, to improve performance of PI controller, this paper proposes SC-FNPI controller adjusted input of PI controller by FNN controller according to operating conditions. Also, this paper proposes efficiency optimization control which is improving efficiency with minimize loss. The SC-FNPI controller proposed in this paper is compared control performance with conventional FNN and PI controller about command speed, load torque and inertia variation. And the efficiency optimization control is compared with $i_d=0$ control about loss and efficiency. The SC-FNPI controller proposed in this paper shows more excellent control performance for rising time, overshoot and steady-state error. Also efficiency optimization control is increased efficiency by reducing loss.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.24 no.6
• /
• pp.123-130
• /
• 2010

In general, a design method based on the equivalent magnetic circuit has been used for basic design of Interior Permanent Magnet Synchronous Motor(IPMSM). However, the equivalent magnetic circuit method has difficulty in considering the arrangement of PM. IPMSM has high degree of freedom for PM rotor design. In this paper, we proposed the multiobjective optimal design method considering the arrangement of PM for the double-layer PM rotor structure that minimizes the torque ripple as well as maximizes the torque of IPMSM. The design variables of double-layer PM rotor structure are obtained from the Response Surface Method. Torque and torque ripple were calculated by Finite Element Method.

• The Transactions of the Korean Institute of Power Electronics
• /
• v.17 no.3
• /
• pp.213-221
• /
• 2012

In a PWM inverter-fed IPMSM (Interior Permanent Magnet Synchronous Motor) drive, a dead time is inserted to prevent a breakdown of switching device caused by the short-circuit of DC link. This distorts the inverter output voltage resulting in a current distortion and torque ripple. In addition to the dead time, nonlinearity exists in switching devices of the PWM inverter, which is generally dependent on operating conditions such as the temperature, DC link voltage, and current. The voltage disturbance caused by the dead time and inverter nonlinearity directly influences on the inverter output performance, and it is known to be more severe at low speed. In this paper, a new compensation scheme for the dead time and inverter nonlinearity under the parameter variation is proposed for a PWM inverter-fed IPMSM drive. The overall system is implemented using DSP TMS320F28335 and the validity of the proposed algorithm is verified through the simulation and experiments.

• Journal of the Korean Society for Precision Engineering
• /
• v.31 no.2
• /
• pp.105-112
• /
• 2014

This paper proposes a novel $H{\infty}$ T-S Fuzzy controller with a weighted integral action for Interior Permanent Magnet Synchronous Motor(IPMSM) which have nonlinear dynamics. The $H{\infty}$ T-S Fuzzy controller is used for the robustness of nonlinear systems and the weighted integral action is used for the tracking problem and the improvement of control performance. A T-S Fuzzy controller is designed by combining the local controllers with the overall stability, and LMI(Linear Matrix Inequality)is used to determine the gains of linear controllers. The tracking problem of IPMSM is changed into regulator problem by introducing the integral action and the weighting factor gives flexibility to a $H{\infty}$ fuzzy controller.

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

• Journal of Electrical Engineering and Technology
• /
• v.13 no.2
• /
• pp.752-760
• /
• 2018

In this paper, interior permanent magnet synchronous motor (IPMSM) design for sensorless drive, considering magnetic neutral point shift according to magnetic saturation, has been proposed. Sensorless control was divided into a method based on inductance and a method based on back induced voltage. Because induced voltage is very small at zero or low speed, error in rotor initial position estimation may occur. Using the ratio of saliency addresses this problem. When using high-frequency injections at low speed, the rotor's initial position is estimated at the smallest portion of the inductance. IPMSM has the minimum inductance at the d-axis. However, if magnetic saturation leads to magnetic neutral point variation, following the load current change, there is a change in the minimum point of inductance. In this case, it can lead to failure of initial rotor position estimation. As a result, it is essential that the blocking design has an inductance minimum point shift. As such, in this study, an IPMSM design method, by blocking magnetic neutral point change, has been proposed. After determining the inductance profile based on the finite element analysis (FEA), the results of proposed method were verified.

• Journal of Electrical Engineering and Technology
• /
• v.13 no.1
• /
• pp.451-459
• /
• 2018

This work presents an approach for modeling of electric vehicle considering the vehicle dynamics, drive train, rotational wheel and load dynamics. The system is composed of IPMSM (Interior Permanent Magnet Synchronous Motor) coupled with the wheels through a drive train. Generally, IPMSM is controlled by ordinary PID controllers. Performance of the ordinary PID controller is not satisfactory owing to the difficulties of optimal gain selections. To overcome this problem, a new type of fuzzy logic gain tuner for PID controllers of IPMSM is required. Therefore, in this paper fuzzy logic based gain tuning method for PID controller is proposed and compared with some previous control techniques for the better performance of electric vehicle with an optimal balance of acceleration, speed, travelling range, improved controller quality and response. The model was developed in MATLAB/Simulink, simulations were carried out and results were observed. The simulation results have proved that the proposed control system works well to remove the transient oscillations and assure better system response in all conditions.

• Proceedings of the KIEE Conference
• /
• 2009.04b
• /
• pp.225-227
• /
• 2009

The conventional fixed gain PI controller is very sensitive to step change of command speed, parameter variation and load disturbances. The precise speed control of interior permanent magnet synchronous motor(IPMSM) drive becomes a complex issue due to nonlinear coupling among its winding currents and the rotor speed as well as the nonlinear electromagnetic developed torque. Therefore, there exists a need to tune the PI controller parameters on-line to ensure optimum drive performance over a wide range of operating conditions. This paper is proposed artificial intelligent-PI(AIPI) controller of IPMSM drive using adaptive learning mechanism(ALM) and fuzzy neural network(FNN). The proposed controller is developed to ensure accurate speed control of IPMSM drive under system disturbances and estimation of speed using artificial neural network(ANN) controller. The PI controller parameters are optimized by ALM-FNN at all possible operating condition in a closed loop vector control scheme. The validity of the proposed controller is verified by results at different dynamic operating conditions.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.19 no.8
• /
• pp.85-93
• /
• 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 posed 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.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
• /
• 2009.05a
• /
• pp.420-423
• /
• 2009

The conventional fixed gain PI controller is very sensitive to step change of command speed, parameter variation and load disturbances. The precise speed control of interior permanent magnet synchronous motor(IPMSM) drive becomes a complex issue due to nonlinear coupling among its winding currents and the rotor speed as well as the nonlinear electromagnetic developed torque. Therefore, there exists a need to tune the PI controller parameters on-line to ensure optimum drive performance over a wide range of operating conditions. This paper is proposed hybrid intelligent-PI(HIPI) controller of IPMSM drive using adaptive learning mechanism(ALM) and fuzzy neural network(FNN). The proposed controller is developed to ensure accurate speed control of IPMSM drive under system disturbances and estimation of speed using artificial neural network(ANN) controller. The PI controller parameters are optimized by ALM-FNN at all possible operating condition in a closed loop vector control scheme. The validity of the proposed controller is verified by results at different dynamic operating conditions.