• Proceedings of the KIEE Conference
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• 2001.04a
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• pp.285-287
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• 2001

To obtain a high performance in a vector controlled induction machine, it is essential to know the instantaneous position of the rotor flux which depends on the rotor time constant. But the rotor time constant mainly varies due to the temperature rise in the motor winding, so real time compensating algorithm is necessary. This paper proposes that it uses short duration pulses added to the constant flux command current and then resultant torque command current produced by speed controller is utilized for the rotor resistance estimation. This method has advantages with a low computational requirement and does not require voltage sensors. The proposed method is proved by simulations.

• Journal of Power Electronics
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• v.16 no.6
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• pp.2173-2181
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• 2016

Permanent magnet synchronous motors (PMSMs) have higher torque and superior output power per volume than other types of AC motors. They are commonly used for applications that require a large output power and a wide range of speed. For precise control of PMSMs, knowing the accurate position of the rotor is essential, and normally position sensors such as a resolver or an encoder are employed. On the other hand, the position sensors make the driving system expensive and unstable if the attached sensor malfunctions. Therefore, sensorless algorithms are widely researched nowadays, to reduce the cost and cope with sensor failure. This paper proposes a sensorless algorithm that can be applied to a wide range of speed. The proposed method features a robust operation at low-speed as well as high-speed ranges by employing a gain adjustment scheme and intermittent voltage pulse injection method. In the proposed scheme the position estimation gain is tuned by a closed loop manner to have stable operation in tough driving environment. The proposed algorithm is fully verified by various experiments done with a 1 kW outer rotor-type PMSM.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.51 no.2
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• pp.69-76
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• 2002

To obtain a high performance in a vector controlled induction motor, it is essential to know the instantaneous position of the rotor flux which depends on the rotor time constant. But the rotor time constant mainly varies due to the temperature rise in the motor winding, so real time compensating algorithm is necessary. This paper proposes that it uses short duration pulses added to the constant flux command current and then resultant torque command current produced by speed controller is utilized for the rotor resistance estimation. This method has advantage with a low computational requirement and does not require voltage sensors. The proposed method is proved by simulations and experimentals.

• Journal of Power Electronics
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• v.12 no.6
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• pp.1003-1010
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• 2012

Recent power electronics and variable-frequency motor drive technologies have been applied to air conditioners to improve efficiency and power density. However, the mechanical vibrations and acoustic noise resulting from the compressor still remain as a serious problem. This paper presents the development and implementation of an online disturbance state-filter for the suppression of multiple unknown and time-varying vibrations of air conditioning systems. The proposed design has a form of the state-filter based on a Luenburger-style closed-loop speed observer. An active vibration decoupling strategy with an estimated disturbance is provided, which manipulates a motor torque command. Since the proposed estimation does not require any additional transducers or hardware for obtaining real-time information upon disturbances, it is suitable for retrofitting industrial air conditioners.

• Journal of Power Electronics
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• v.12 no.5
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• pp.758-768
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• 2012

This paper presents the modeling and position-sensorless vector control of a dual-airgap axial flux permanent magnet (AFPM) machine optimized for use in flywheel energy storage system (FESS) applications. The proposed AFPM machine has two sets of three-phase stator windings but requires only a single power converter to control both the electromagnetic torque and the axial levitation force. The proper controllability of the latter is crucial as it can be utilized to minimize the vertical bearing stress to improve the efficiency of the FESS. The method for controlling both the speed and axial displacement of the machine is discussed. An inherent speed sensorless observer is also proposed for speed estimation. The proposed observer eliminates the rotary encoder, which in turn reduces the overall weight and cost of the system while improving its reliability. The effectiveness of the proposed control scheme has been verified by simulations and experiments on a prototype machine.

• Proceedings of the KIEE Conference
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• 1996.11a
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• pp.302-304
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• 1996

In this paper, a new algorithm for sensorless speed control of switched reluctance motor (SRM) is proposed. It is based on the measurement of initial rate of change of phase current. The initial rate of rise of phase current mainly depends up on the phase inductance at the time instant when the phase is excited. Therefore, the measurement of initial di/dt permits rotor position estimation, which is used to trigger the next phase. In the proposed technique, there is no need to generate additional current pulses when a phase is not excited. Therefore, this scheme does not introduce the unwanted braking torque. Also, only one current measurement is made every time a phase is excited. This reduces the computational load on the micro-controller and enhances the speed range of the sensorless drive. By using this scheme it is possible to implement the sensorless control of SRM using low cost micro-controller.

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

• The Journal of Korea Robotics Society
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• v.1 no.1
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• pp.81-88
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• 2006

Control of a robot manipulator in contact with the environment is usually conducted by the direct feedback control using a force-torque sensor or the indirect impedance control. In these methods, however, the control algorithms become complicated and the performance of position and force control cannot be improved because of the mechanical properties of the passive components. To cope with such problems, redundant actuation has been used to enhance the performance of position control and force control. In this research, a Double Actuator Unit (DAU) is proposed, with which the force control algorithm can be simplified and can make the robot ensure the safety during the external collision. The DAU is composed of two actuators; one controls the position and the other modulates the joint stiffness. Using this unit, it is possible to independently control the position and stiffness. The DAU based on the planetary gears is investigated in this paper. Performance using the DAU is also verified by various experiments. It is shown that the manipulator using this mechanism provides better safety during the impact with the environment by reducing the joint stiffness appropriately on detecting the collision of a manipulator.

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

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.5
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• pp.3030-3034
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• 2015

It is important to consider powertrain cooling performance, when engine is applied to new vehicle. If the performance is poor, engine will be damaged by overheating. But, the development timing of engine is faster than timing of vehicle, it is difficult to test the cooling performance of new engine and vehicle. In this study the powertain cooling performance was estimated with some test and calculation data. First, the heat rejection test was conducted. From this test, the heat rejection data at required rpm and load was acquired. Second, coolant flow test was conducted. From this test coolant flow rate to radiator was measured. Then engine torque and rpm was calculated from vehicle load and speed. Vehicle load and speed was calculated from test mode. Then by comparing these data, the powertrain cooling performance was estimated.