• 전력전자학회논문지
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• 제1권1호
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• pp.12-19
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• 1996

The purpose of this paper is the improvement of the speed control characteristics of induction machines suited the low resolution incremental-type encoder in a low speed region. In order to improve the control characteristics in a low speed range, we propose that the instantaneous speed control method by the instantaneous speed detection which is implemented by the disturbance torque observer. Also, in case of the speed control by the instantaneous speed detection, the simple estimation method of the mechanical inertia moment is proposed. We will the carry out the mathematical verification of the proposed theory by the theoretic advisement connected with the convergence relationship of the estimated inertia moment to the real mechanical inertia moment. Computer simulations and experiments by the IGBT inverter adopting DSP is performed to verify the proposed method.

• 전기학회논문지P
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• 제56권2호
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• pp.74-82
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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 efficiency optimization control of IPMSM drive using adaptive fuzzy learning control fuzzy neural network (AFLC-FNN) controller. In order to maximize the efficiency in such applications, this paper proposes the optimal control method of the armature current. The controllable electrical loss which consists of the copper loss and the iron loss can be minimized by the optimal control of the armature current. The minimization of loss is possible to realize efficiency optimization control for the proposed IPMSM. The optimal current can be decided according to the operating speed and the load conditions. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using AFLC-FNN controller. Also, this paper proposes speed control of IPMSM using AFLC-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 AFLC-FNN controller, the operating characteristics controlled by efficiency optimization control are examined in detail.

• 조명전기설비학회논문지
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• 제20권1호
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• pp.57-64
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• 2006

본 논문에서는 유도전동기의 벡터제어를 위한 ANN 센서리스 제어와 속도제어를 위한 AFLC를 제안하였다. AFLC 설계는 적응 메카니즘을 통해 퍼지 룰 베이스의 수정자를 갱신하여 실행할 수 있고 유도 전동기의 속도 추정을 위한 ANN 센서리스 제어는 BPA를 통해 수행하였다. 유도전동기의 지령속도와 실제속도는 BPA를 통해 그 오차를 줄일 수 있고, 이러한 알고리즘은 다른 전동기 드라이브에 적용이 용이하다. 본 논문에서 제시한 AFLC 및 ANN 제어의 응답특성을 분석하고 그 결과를 제시한다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1478-1481
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• 2005

As the model of most practical system cannot be obtained, the practice of typical control method is limited. Accordingly, numerous artificial intelligence control methods have been used widely. Fuzzy control and neural network control have been an important point in the developing process of the field. This paper is proposed adaptive fuzzy-neural network based on the vector controlled interior permanent magnet synchronous motor drive system. The fuzzy-neural network is first utilized for the speed control. A model reference adaptive scheme is then proposed in which the adaptation mechanism is executed using fuzzy-neural network. Also, this paper is proposed estimation of speed of interior permanent magnet synchronous motor 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. This paper is proposed the analysis results to verify the effectiveness of the new method.

• 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.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 하계학술대회 논문집 B
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• pp.1251-1253
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• 2004

This paper is proposed a fuzzy neural network controller based on the vector controlled surface permanent magnet synchronous motor(SPMSM) drive system. 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 SPMSM using neuro-fuzzy control(NFC) 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 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.

• 한국심리학회지 : 문화 및 사회문제
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• 제11권2호
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• pp.31-46
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• 2005

본 연구는 운전 중 발생하는 청각 자극의 유형 및 정도가 운전자가 지각하는 주관적 속도감에 어떠한 영향을 미치는지를 알아보고자 수행되었다. 본 연구에서 사용된 청각 자극은 엔진 소음과 음악 소리였으며 전자는 자동차의 속도에 따라 함께 변하는 청각 단서인 반면 후자는 자동차의 속도와는 무관하게 변화하는 비단서 소음으로 사용되었다. 엔진 소음의 강도만 조작되어 제시된 실험 1에서는 엔진 소음의 크기에 따라 운전자가 느끼는 주관적 속도감이 다르게 나타나 운전자들은 엔진 소음이 작을수록 기준속도보다 더 빨리 주행한 반면 엔진 소음이 클수록 더 천천히 주행하는 것이 관찰되었다. 실험 2에서는 엔진 소음과 음악 소리를 모두 들려주었는데, 그 결과 엔진 소음의 크기변화에 따른 주관적 속도감의 차이는 여전히 나타났으나 음악 소리의 크기변화에 따른 속도감의 차이는 보이지 않았다. 또한 실험 1과 2를 통합하여 음악 소리의 유무효과를 분석해 본 결과 음악 소리가 엔진 소음을 차폐시킬 만큼 크고 엔진 소음이 작을 경우에는 음악 소리가 없는 경우보다 속도를 더 내는 것으로 나타났다. 이는 음악 소리가 속도감에 영향을 주는 직접요인은 아니지만 간접적으로 영향을 미친다는 가능성을 시사한다.

• 전력전자학회:학술대회논문집
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• 전력전자학회 1997년도 전력전자학술대회 논문집
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• pp.171-176
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• 1997

This paper presents a design method of the continuous inertial binary observer which includes the rotor flux and speed estimations. The sliding observer based on the variable structure theory ensures the robustness of disturbance and is applied for the method to keep an insensitivity for the variations of parameter. Sliding observer, however, has a high-frequency chattering deteriorating the state estimation performance. To reduce the chattering on the sliding surface in sliding observer and improve the estimation performance, binary observer scheme which has main advantages such as the absence of high-frequency chattering and the finite gains is applied in this paper. Computer simulation results show the effectiveness of binary observer proposed here for the induction motor drives.

• 반도체디스플레이기술학회지
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• 제14권2호
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• pp.35-40
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• 2015

This paper presents the sensorless position control of the Permanent Magnet Synchronous Motor (PMSM) using stator flux estimation and Phase Lock Loop (PLL). The field current and the torque current are required in order to perform the vector control of the PMSM. At this time, it is necessary for the torque to know the exact position of the magnetic flux generated by the permanent magnet, because the torque must be applied torque current in the direction orthogonal to the permanent magnet. In general the speed of the PMSM is controlled by using a magnetic position sensor. However, this paper, we estimates the stator flux by using the PLL method without the magnetic position sensor. This method is simple and easy, in addition it has the advantage of a stabile estimation of the rotor. Finally the proposed algorithm was confirmed by experimental results and showed the good performance.

• International journal of advanced smart convergence
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• 제7권2호
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• pp.95-100
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• 2018

In this paper, we propose a binarized multi-scale module to accelerate the speed of the pose estimating deep neural network. Recently, deep learning is also used for fine-tuned tasks such as pose estimation. One of the best performing pose estimation methods is based on the usage of two neural networks where one computes the heat maps of the body parts and the other computes the part affinity fields between the body parts. However, the convolution filtering with a large kernel filter takes much time in this model. To accelerate the speed in this model, we propose to change the large kernel filters with binarized multi-scale modules. The large receptive field is captured by the multi-scale structure which also prevents the dropdown of the accuracy in the binarized module. The computation cost and number of parameters becomes small which results in increased speed performance.