• Journal of Power Electronics
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• 제11권4호
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• pp.527-532
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• 2011

Due to the need for improved fuel consumption and the trend towards increasing the electrical content in automobiles, integrated starter generator (ISG) systems are being considered by the automotive industry. In this paper, in order to change the conventional generator of a vehicle, a belt driven integrated starter generator is considered. The overall ISG system, the design considerations for the claw pole type AC electric machine and a low voltage very high current power stage implementation are discussed. Test data on the low voltage claw pole type machine is presented, and a large current voltage source DC/AC inverter suitable for low voltage integrated starter generator operation is also presented. A metal based PCB (Printed Circuit Board) power unit to attach the 4-parallel MOS-FETs is used to achieve extremely high current capability. Furthermore, issues related to the torque assistance during vehicle acceleration and the generation/regeneration characteristics are discussed. A prototype with the capability of up to 1000 A and 27 V is designed and built to validate the kilo-amp inverter.

• 대한전기학회논문지:전기기기및에너지변환시스템부문B
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• 제55권2호
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• pp.89-97
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• 2006

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 drive using learning mechanism-fuzzy neural network(LM-FNN) controller 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 $i_{d}$ for maximum torque operation is derived. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using LM-FNN controller and ANN controller. 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 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 LM-FNN and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper is proposed the analysis results to verify the effectiveness of the LM-FNN and ANN controller.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2008년도 하계학술대회 논문집
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• pp.106-108
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• 2008

This paper deals with the design for speed controller to drive PMSM by matrix converter without DC-link circuit as the power conversion system of AC servo motor drive. To design the speed controller of PMSM drive, the closed-loop transfer function of speed controller is calculated and then the frequency-domain response characteristics are analyzed by bode plot using Matlab. Based on the results by bode plot, the speed control gains are determined. As the real effects of controller designed in the frequency-domain display in the time-domain, the performance of speed controller is confirmed by the step response of speed controller. The design examples are shown and its validity of the design method mentioned in the paper is verified through PSIM simulation.

• 제어로봇시스템학회논문지
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• 제16권3호
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• pp.249-255
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• 2010

The paper proposes a new type of robust speed control strategy for permanent magnet synchronous motor by using PD-sliding mode hybrid control. The PD control has a good performance in the transient region while the sliding mode controller provides the robustness against system uncertainties. Taking advantages of the two control strategies, the proposed control method utilizes the PD control in the approaching region to the sliding surface and the sliding mode control near at the sliding surfaces. The chattering problem of the sliding mode controller is eliminated by applying the saturation function for the switching function of the sliding mode control. The stability of the sliding mode control is verified by using Lyapunov function with the proper selection of variable gains. It is shown that with this simple switching algorithm, stability of the overall hybrid control system is ensured. Through the simulations, the PD-sliding mode algorithm is shown to have a good performance in the transient response as well as being robust against disturbances. The robustness of the PD-sliding mode algorithm is further demonstrated against various external disturbances in the real experiments of SPMSM motor control.

• 대한전기학회논문지:시스템및제어부문D
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• 제55권3호
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• pp.110-114
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• 2006

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. In this paper maximum torque control of IPMSM drive using artificial intelligent(AI) controller is proposed. 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. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using AI controller. This paper is proposed speed control of IPMSM using adaptive learning mechanism fuzzy neural network(ALM-FNN) and estimation of speed 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 proposed control algorithm is applied to IPMSM drive system controlled ALM-FNN and ANN controller, 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 AI controller.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 하계학술대회 논문집 B
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• pp.1248-1250
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• 2004

This paper is proposed adaptive fuzzy-neural network controller(NFC) for speed control of surface permanent magnet synchronous motor(SPMSM) drive. The design of this algorithm based on NFC that is implemented using fuzzy control and neural network. This controller uses fuzzy rule as training patterns of a neural network. Also, this controller uses the back-propagation method to adjust the weights between the neurons of neural network in order to minimize the error between the command output and actual output. A model reference adaptive scheme is proposed in which the adaptation mechanism is executed by fuzzy logic based on the error and change of error measured between the motor speed and output of a reference model. The control performance of the adaptive NFC is evaluated by analysis for various operating conditions. The results of analysis prove that the proposed control system has strong high performance and robustness to parameter variation, and steady-state accuracy and transient response.

• Journal of Power Electronics
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• 제16권1호
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• pp.249-258
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• 2016

Given that elastic connection is often used between motor drives and load devices in industrial applications, vibration often occurs at the load side. Vibration suppression is a crucial problem that needs to be addressed to achieve a high-performance servo-control system. Scholars have presented many strategies to suppress vibration. In this study, we propose a method to diminish vibration by using a torque feed-forward and disturbance torque observer. We analyze the system performance and explain the principle of the proposed vibration suppression method based on the transfer functions of the system. The design of controller parameters is another important issue in practical applications. We accordingly provide a succinct outline of the design specifications based on the coefficient diagram method. Furthermore, we build a model under the Simulink environment and conduct experiments to validate the proposed method. Results show that speed and position vibrations are successfully suppressed by the proposed method.

• 전기학회논문지
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• 제66권2호
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• pp.339-346
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• 2017

This paper proposes a sensorless control method to improve the performance of an internal permanent magnet synchronous motor (IPMSM) control by using a full-order flux observer in a wide speed range. The conventional sensorless control method uses a constant gain for high performance at low-speed region. However, this method has drawbacks such as an increased angle error and current ripple in the high-speed region due to the fixed gain value. In order to overcome this problem, the gain of the full-order flux observer is changed by considering the angle error in the whole speed range. The proposed method minimizes the angle error for each region of the speed range by applying a relevant gain value, which improves the current ripple reduction and motor noise cancellation. The validity of proposed sensorless control method is verified by a simulation and an experiment.

• 한국조명전기설비학회:학술대회논문집
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• 한국조명전기설비학회 2006년도 춘계학술대회 논문집
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• pp.309-314
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• 2006

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 maximum torque control of IPMSM drive using adaptive learning fuzzy neural network and artificial neural network. This control method is applicable over the entire speed range which considered the limits of the inverter's current md 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 adaptive teaming fuzzy neural network and artificial neural network. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also, this paper proposes speed control of IPMSM using adaptive teaming fuzzy neural network and estimation of speed using artificial neural network. 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 adaptive teaming fuzzy neural network and artificial neural network, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper proposes the analysis results to verify the effectiveness of the adaptive teaming fuzzy neural network and artificial neural network.

• 전력전자학회논문지
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• 제22권2호
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• pp.175-180
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• 2017

The measurement of three-phase current is important to control the instantaneous torque of a interior permanent magnet synchronous motor(IPMSM) using a three-phase inverter. Therefore, shunt resistors are used in low-cost motor-driving systems to measure three-phase current instead of additional current sensors that are too expensive for these systems. However, in certain regions of a space vector plane, shunt resistors cannot reconstruct three-phase current in high-speed driving mode. In this paper, predictive current control is used to compensate for the three-phase current in those regions, which results in a reduction of current ripple in a three-shunt sensing inverter(TSSI) and torque ripple in IPMSM.