• Journal of Power Electronics
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• v.3 no.1
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• pp.33-39
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• 2003

This paper presents the self-commissioning for surface-mounted permanent magnet synchronous motor. The proposed strategy executes three tests with a vector controlled inverter drive system. To do this, synchronous d-q axes currents are appropriately controlled for each test. From the three tests, armature resistance, armature inductance, equivalent iron loss resistance, and emf coefficient are identified automatically. The validity of the proposed strategy is confirmed by experimental results.

• Proceedings of the KIPE Conference
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• 2001.07a
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• pp.131-134
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• 2001

The accurate identification of the motor parameters is crucially important to achieve high dynamic performance of induction motors. In this paper, the motor parameters such as rotor resistance, stator(rotor) leakage inductance, mutual inductance are measured for torque monitoring and indirect vector control. To demonstrate the practical significance of the results, some experimental results are presented.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2013.10a
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• pp.273-274
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• 2013

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.341-344
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• 1997

This paper presents a dynamics failure detection algorithm developed for the two-motor-driven electric vehicle system. The algorithm is based on the application of the fault detection filter. The fault detection includes the identification of sudden pressure drops of the two rear tires in driving axis and dynamics faults of the two inverter-motor-paired actuators An E.V. dynamics simulator is developed, which includes the modeling of the E.V. dynamics as well as the driving dynamics. The simulator, which allows the generation of various fault situations, is utilized in the verification of the developed fault detection algorithm. The results of the simulations are also presented.

• Journal of Electrical Engineering and Technology
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• v.4 no.3
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• pp.389-395
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• 2009

Industrial motors are subject to incipient faults which, if undetected, can lead to motor failure. The necessity of incipient fault detection can be justified by safety and economical reasons. The technology of artificial neural networks has been successfully used to solve the motor incipient fault detection problem. This paper develops inexpensive, reliable, and noninvasive NN based incipient fault detection scheme for small and medium sized induction motors. Detailed design procedure for achieving the optimal NN model and Principal Component Analysis for dimensionality reduction is proposed. Overall thirteen statistical parameters are used as feature space to achieve the desired classification. GFFD NN model is designed and verified for optimal performance in fault identification on experimental data set of custom designed 2 HP, three phase 50 Hz induction motor.

• Proceedings of the KIEE Conference
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• 2003.10b
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• pp.203-205
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• 2003

This paper is proposed a newly developed approach to identify the mechanical speed of an induction motor based on artificial neural networks technique. 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. This paper is proposed the theoretical analysis as well as the simulation results to verify the effectiveness of the new method.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.239-241
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• 1992

In this paper, back-propagation neural network is used for the identification and trajectory control of a DC motor. The neural network is trained to identify the unknown nonlinear dynamics of the motor and load and the trained neural network is used for speed control of the DC motor to have good performance. Simulation results show the good performance of the control system based on the neural network under arbitrarily chosen speed trajectories.

• Proceedings of the KIEE Conference
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• 2006.07b
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• pp.861-864
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• 2006

냉장고에 적용된 BLDC Fan Motor에 있어서 공진소음의 원인에 대하여 연구하였다. 연구에 있어서 종래의 가진 방법을 적용하기 곤란한 구조를 가진 소형 Fan & Rotor System의 동 특성 실험에서 전자 가진법 및 Microphone을 이용한 고유진동수 계측방법을 사용하고 그 타당성을 보였으며 연구결과, Pan과 Rotor System의 비틀림 고유진동수와 Fan의 굽힘 고유진동수가 BLDC Motor의 Commutating Torque Ripple에 의한 가진 주파수와 일치하여 공진이 발생하는 것으로 분석 되었으며 이와 관련된 주요 인자를 분석하고 대안을 제시하였다.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.705-710
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• 1989

A practical method of identifying the inertial parameters, viscous friction and Coulomb friction of a robot is presented. The parameters in the dynamic equations of a robot are obtained from the measurements of the command voltage and the joint position of the robot. First, a dynamic model of the integrated motor and manipulator is derived. An off line parameter identification procedure is developed and applied to the University of Minnesota Direct Drive Robot. To evaluate the accuracy of the parameters the dynamic tracking of robot was tested. The trajectory errors were significantly reduced when the identified dynamic parameters were used.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.127-130
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• 1990

The fuzzy logic controller which can be applied to various industrial processes is quite often dependent on the heuristics of the experienced operator. The operator's knowledge is often uncertain. Therefore an incorrect control rule on the basis of the operator's information is a cause of bad performance of the system. This paper proposes a new self-learning fuzzy control method by the fuzzy system identification using the data pairs of input and output and arbitrary initial relation matrix. The position control of a DC servo motor model is simulated to verify the effectiveness of the proposed algorithm.