• Journal of IKEEE
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• v.24 no.4
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• pp.1104-1108
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• 2020

This paper propose a variable proportional resonant feedforward algorithm for reducing the speed ripple of a three-phase permanent magnet synchronous motor. In general, the torque ripples can be generated by electrical pulsation due to current measurement errors and dead time and mechanical pulsation because of rotor eccentricity and eccentric load. These torque pulsations can cause speed pulsations of the motor and degrade the operating performance of the motor drive system. Therefore, in this paper, the factors of the speed ripple is analyzed and an algorithm to reduce the speed ripple is proposed. The proposed algorithm applied a variable proportional resonant controller in order to reduce the specific operating frequency included in the speed pulsation, and utilized a feedforward compensation controller structure to perform the compensation operation. The proposed algorithm is verified through various experiments.

• Journal of IKEEE
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• v.26 no.4
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• pp.596-601
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• 2022

The application of the EGR system is increasing according to the recent trend of conversion to green-ships. EGR blower, one of the core parts of the EGR, consists of aerodynamic system and e-motor and inverter and etc. For the e-motor, a permanent magnet type synchronous motor with high energy density and excellent efficiency is applied recently. Small and medium-sized enterprises trying to develop the e-motors, however, for marine inverters mostly developed by global advanced companies due to the rigid classification certification and technical difficulties. One of disadvantage of universal inverters is that when optimal control fails, it is difficult to find the cause from user's point of view. Therefore, in this study, optimal controllers(Current vector contol and Tracking observer) for SPMSM for EGR blower was designed and verified to analyze the causes of failure of optimal control of universal inverter.

• Journal of Power Electronics
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• v.5 no.2
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• pp.129-141
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• 2005

A high-performance robust hybrid speed controller for a permanent-magnet synchronous motor (PMSM) drive with an on-line trained neural-network model-following controller (NNMFC) is proposed. The robust hybrid controller is a two-degrees-of-freedom (2DOF) integral plus proportional & rate feedback (I-PD) with neural-network model-following (NNMF) speed controller (2DOF I-PD NNMFC). The robust controller combines the merits of the 2DOF I-PD controller and the NNMF controller to regulate the speed of a PMSM drive. First, a systematic mathematical procedure is derived to calculate the parameters of the synchronous d-q axes PI current controllers and the 2DOF I-PD speed controller according to the required specifications for the PMSM drive system. Then, the resulting closed loop transfer function of the PMSM drive system including the current control loop is used as the reference model. In addition to the 200F I-PD controller, a neural-network model-following controller whose weights are trained on-line is designed to realize high dynamic performance in disturbance rejection and tracking characteristics. According to the model-following error between the outputs of the reference model and the PMSM drive system, the NNMFC generates an adaptive control signal which is added to the 2DOF I-PD speed controller output to attain robust model-following characteristics under different operating conditions regardless of parameter variations and load disturbances. A computer simulation is developed to demonstrate the effectiveness of the proposed 200F I-PD NNMF controller. The results confirm that the proposed 2DOF I-PO NNMF speed controller produces rapid, robust performance and accurate response to the reference model regardless of load disturbances or PMSM parameter variations.

• Journal of the Korean Magnetics Society
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• v.25 no.3
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• pp.92-100
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• 2015

Electric Vehicle (EV) can be categorized by the driving method into in-wheel and in-line types. In-wheel type EV does not have transmission shaft, differential gear and other parts that are used in conventional cars, which simplifies and lightens the structure resulting in higher efficiency. In this paper, design method for in-wheel motor for automobiles using Parameter Map is proposed, and motor with continuous power of 5 kW is designed, built and its performance is verified. To decide the capacity of the in-wheel motor that meets the automobile's requirement, Vehicle Dynamic Simulation considering the total mass of vehicle, gear efficiency, effective radius of tire, slope ratio and others is performed. Through this step, the motor's capacity is decided and initial design to determine the motor shape and size is performed. Next, the motor parameters that meet the requirement is determined using parametric design that uses parametric map. After the motor parameters are decided using parametric map, optimal design to improve THD of back EMF, cogging torque, torque ripple and other factors is performed. The final design was built, and performance analysis and verification of the proposed method is conducted by performing load test.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2007.11a
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• pp.313-318
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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 maximum torque control of IPMSM drive using adaptive fuzzy neural network controller and artificial neural network(ANN). 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 Adaptive-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 reposes speed control of IPMSM using Adaptive-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 a lied to IPMSM drive system controlled Adaptive-FNN and ANN controller, 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-FNN and ANN controller.

• The Transactions of the Korean Institute of Power Electronics
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• v.11 no.1
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• pp.7-13
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• 2006

Linear compressors with a free piston driven by a linear motor are widely attention in the cooling apparatus such as refrigerator due to the high efficiency The stroke of piston in the linear compressor driven by LPMSM (Linear Permanent-Magnet Synchronous Motor) can be obtained from Integrating the input voltage and current of LPMSM, but it may be diverged due to dc components in the voltage and current. The strategy to prevent the divergence of stroke using both the high-pass filter and do offset compensation was suggested. The equations for the magnitude and phase of the stroke and also dc offset including the stroke are derived as a function of the cut-off frequency of HPF. The accurate stroke of a piston can be calculated by compensating for dc offset. The performance of the newly developed stroke calculation scheme has been verified by experimentally on a linear compressor drive system, where the control was implemented by a 16-bit DSP.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.21 no.5
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• pp.32-43
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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 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 and voltage rated value. This paper proposes speed control of IPMSM using adaptive learning 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 learning 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 learning fuzzy neural network and artificial neural network.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.23 no.8
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• pp.33-40
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• 2009

This paper presents the high performance control of interior permanent magnet synchronous motor(IPMSM) using space vector(SV) PWM method based on hybrid artificial intelligent(HAI) controller. The HAI controller combines the advantages between adaptive fuzzy control and neural network The SV PWM method is applied to a speed control system of motor in the industry field until now and is feasible to improve harmonic rate of output current, switching frequency and response characteristics. This HAI controller is used instead of conventional PI controller in order to solve problems happening when calculating a reference voltage. The HAI controller improves speed performance by hybrid combination of reference model-based adaptive mechanism method, fuzzy control and neural network. This paper analyzes response characteristics of parameter variation, steady-state and transient-state using proposed HAI controller and this controller compares with conventional fuzzy neural network(FNN) and PI controller. Also, this paper proves validity of HAI controller.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.19 no.3
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• pp.66-74
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• 2005

This paper presents an implementation of digital servo speed control system of SPMSM(Surface-mounted Permanent Magnet Synchronous Motor) for industrial application with Direct Torque Control(DTC) using TMS320F2812 DSP. Although, the vector control scheme is adapted in many industrial servo system, but the DSP calculation ratio is increased by reference frame transformation and SVPWM of vector control. Therefore, this paper investigate the possibility of DTC scheme for industrial servo drive system instead of vector control scheme. DSP calculation ratio is compared between vector control and DTC algorithm in addition to the characteristic of speed control response. The suggested SPMSM control system shows the possibility of DTC scheme for industrial servo motor drive system instead of a vector control algorithm.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.22 no.1
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• pp.40-49
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• 2008

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications and so of due to their excellent power to weight ratio. To obtain maximum efficiency in these applications, this paper proposes the neural network control method. The controllable electrical loss which consists of the copper loss and the iron loss can be minimized by the error back propagation algorithm(EBPA) of neural network. The minimization of loss is possible to realize eHciency optimization control for the IPMSM drive. This paper proposes high performance and robust control through a real time calculation of parameter variation such as variation of back emf constant, armature resistance and d-axis inductance about the motor operation. Proposed algorithm is applied IPMSM drive system, prove validity through analysis operating characteristics con011ed by efficiency optimization control.