• The Transactions of the Korean Institute of Power Electronics
• /
• v.24 no.2
• /
• pp.71-77
• /
• 2019

This study proposes a circulating current reduction method that uses high-frequency voltage compensation when carrier phase difference occurs between two inverters in MSIS. In MSIS, inverters are configured in parallel to increase power capacity and to increase efficiency by using inverters only as needed. However, in the parallel inverter structure, circulating current is inevitably generated. Circulating current increases the stress on the switch, adversely affects the current control performance, and renders load sharing difficult. The proposed method compensates for the output voltage reference of the slave module by using the high-frequency voltage so that the switching pattern of each module is matched even in asynchronous carriers. The validity of the proposed method is verified by simulations and experiments with 600 W IPMSM.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.17 no.5
• /
• pp.859-866
• /
• 2022

Active disturbance rejection control is a method in which the disturbance is removed from the controller by estimating the state variable using the Luenberger observer. The Luenberger observer is estimated by defining a nonlinear term including disturbance with constant characteristics in a steady state as a state variable. It can be shown that the speed tracking performance is improved by compensating the estimated state variable to the PI controller and the IP controller. The disturbance removal performance of the tracking control can be confirmed by observing that the estimated state error is within 1.9 [%] in the case of load fluctuation and the steady-state state tracking error converges to zero.

• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.40 no.3
• /
• pp.199-207
• /
• 2003

This paper is proposed new fuzzy controller for high performance of interior permanent magnet synchronous motor(IPMSM) drive. New fuzzy controller take out appropriate amounts of accumulated control input according to fuzzily described situations in addition to the incremental control input calculated by conventional direct fuzzy controller The structures of the proposed controller is motivated by the problems of direct fuzzy controller. The direct controller generally give inevitable overshoot when one tries to reduce rise time of response especially when a system of order higher than one is under consideration. The undesirable characteristics of the direct fuzzy controller are caused by integrating operation of the controller, even though the Integrator itself is introduced to overcome steady state error in response. Proposed controller fuzzily clear out integrated quantities acrording to situation. This paper attempts to provide a thorough comparative insight into the behavior of IPMSM drive with direct and new fuzzy speed controller. The validity of new fuzzy speed controller is confirmed by response results for IPMSM drive system.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
• /
• 2007.11a
• /
• pp.313-318
• /
• 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
• /
• v.11 no.1
• /
• pp.29-37
• /
• 2006

Precise control of interior permanent magnet synchronous motor(IPMSM) over wide speed range is an engineering challenge. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using learning mechanism-fuzzy neural network(LM-FNN) and ANN(artificial neural network) control. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility md numerical processing capability. Also, this paper proposes speed control of IPMSM using LM-FNN and estimation of speed using artificial neural network 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. Analysis results to verify the effectiveness of the new hybrid intelligent control proposed in this paper.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.21 no.5
• /
• pp.32-43
• /
• 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.

• The Transactions of the Korean Institute of Power Electronics
• /
• v.16 no.2
• /
• pp.152-161
• /
• 2011

An interior permanent magnet synchronous motor(IPMSM) has been applied to the electric vehicle due to its high efficiency, compact volume, and wide operating speed ranges. This paper presents the analysis of the flux weakening operating regions at high speeds for the IPMSM that has back emf harmonics. The effect of the back emf harmonics on the motor speed and the maximum torque is analyzed. Also the dq currents for maximum torque operation under the voltage and the current limit conditions are analyzed. The conventional analysis and the presented analysis for the flux weakening operating regions are compared and the maximum torque - speeds characteristics for both analysis are verified through the experiment.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.23 no.8
• /
• pp.33-40
• /
• 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
• /
• v.23 no.8
• /
• pp.57-66
• /
• 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 proposes 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.

• Journal of Electrical Engineering and Technology
• /
• v.11 no.3
• /
• pp.609-617
• /
• 2016

Belt-driven Starter Generator (BSG) differs from other mild hybrid systems as the crankshaft of vehicle are not run off. Motor permits a low-cost method of adding mild hybrid capabilities such as start-stop, power assist, and mild levels of regenerative braking. Wound rotor synchronous motor (WRSM) could be adopted in BSG system for HEV e-Assisted application instead of the interior permanent magnet synchronous motor (IPMSM). In practice, adequate torque is indispensable for starter assist system, and energy conversion should be taken into account for the HEV or EV as well. Particularly, flux weakening control is possible to realize by adjusting both direct axis components of current and field current in WRSM. Accordingly, this paper present an off-line current acquisition algorithm that can reasonably combine the stator and field current to acquire the maximum torque, meanwhile the energy conversion is taken into consideration by losses. Besides, on account of inductance influence by non-uniform air gap around rotor, nonlinear inductances and armature flux linkage against current variation are proposed to guarantee the results closer to reality. A computer-aided method for proposed algorithm are present and results are given in form of the Look-up table (LUT). The experiment shows the validity of algorithm.