• 전기학회논문지
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• 제67권11호
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• pp.1434-1446
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• 2018

This paper presents new speed control strategy for periodic load torque injected in AC motor. If motor drive system has a periodic load torque, it causes a periodic motor speed ripple bringing about vibrations and noises. This paper proposed new control method consisting of PIR(proportional-integral-resonant) controller and repetitive controller. PIR controller controls DC, low frequency and fundamental components and repetitive controller controls other harmonics. The performance has been verified through computer simulations using MATLAB Simulink and experiments.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.347-350
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• 1996

With only the classical PID controller applied to control of a DC motor, a good (target) performance characteristic of the controller can be obtained, if all the model parameters of DC motor and operating conditions such as external load torque, disturbance, etc. are exactly known. However, in case when some of system parameters or operating conditions are uncertain or unknown, the fixed PID controller does not guarantee the good performance which is assumed with precisely known system parameters and operating conditions. In view of this and robustness enhancement of DC motor control system, we propose a PID learning controller which consists of a set of learning rules for PID gain tuning and learning of an auxiliary input. The proposed PID learning controller is shown to drive the state of uncertain DC motor system with unknown system parameters and external load torque to the desired one globally asymptotically. Computer simulation results are given to demonstrate the effectiveness of the proposed PID learning controller, thereby showing whose superiority to the conventional fixed PID controller.

• 전력전자학회논문지
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• 제9권3호
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• pp.253-259
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• 2004

본 논문은 BLDC 전동기의 고성능 속도제어를 위하여 외란 초크 관측기를 기반으로 한 속도 제어 방법에 대하여 기술하였다. 강성이 낮은 로봇 팔이나 추적 응용의 경우 시스템의 안정성 측면에서 속도 제어기의 이득 값을 크게 할 수 없다. 따라서 외란 토크 관측기를 이용한 전향 보상 방법을 이용하였다. 본 방법으로 속도 제어기의 이득을 충분히 크게 할 수 없을 때 외란 토크에 대한 속도 응답 특성을 향상시킬 수 있다. 결과적으로, 고성능 분야의 응용을 위한 BLDC 전동기의 속도 제어가 가능하게 된다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 춘계학술대회 논문집 전기기기 및 에너지변환시스템부문
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• pp.155-157
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• 2006

The paper is proposed maximum torque control of SynRM drive using adaptive learning mechanism-fuzzy neural network(ALM-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. The proposed control algorithm is applied to SynRM drive system controlled ALM-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 ALM-FNN and ANN controller.

• 한국통신학회논문지
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• 제16권11호
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• pp.1186-1193
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• 1991

본 논문에서는 신경회로망을 사용한 로보트 매니퓰레이터의 궤적 제어 방법을 제안하였다. 매니퓰레이터에 가해지는 토크는 신경회로망이 출력인 feedforward 토크와 보조제어기로 사용되는 비례 미분 제어기PD 제어기의 출력인 feedback 토크의 합이다. 제안된 전경 회로망은 다층 신경회로로서 시간 지연 요소를 가지며 PD 제어기의 오차 토크를 사용하여 매니퓰레이터 이동력학 모델을 학습한다. errror backpropagation(BP) 학습 신경회로 제어기를 사용해보므로서 매니퓰레이터 동특성에 대한 정보를 미리 필요로 하지 않으며, 연결 가중치 값에 그러한 정보가 저장된다. 확인될 신경회로망의 특성을 컴퓨터 시뮬레이션을 통하여 입증한다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 제37회 하계학술대회 논문집 B
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• pp.729-730
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• 2006

The paper is proposed maximum torque control of SynRM drive using adaptive fuzzy neural network(A-FNN) controller and artificial neural network(ANN). For each control mode, a condition that determines the optimal d-axis current $i_d$ for maximum torque operation is derived. The proposed control algorithm is applied to SynRM drive system controlled A-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 A-FNN and ANN controller.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 추계학술대회 논문집 전기기기 및 에너지변환시스템부문
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• pp.198-201
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• 2005

The paper is proposed maximum torque control of IPMSM drive using adaptive learning mechanism-fuzzy neural network (ALM-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. The proposed control algorithm is applied to IPMSM drive system controlled ALM-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 verily the effectiveness of the ALM-FNN and ANN controller.

• 전기학회논문지
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• 제66권11호
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• pp.1663-1668
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• 2017

A BLDC motor with higher number of phases has several advantages, compared to the conventional three-phase BLDC motors. It can reduce the commutation torque ripple and the iron loss without increasing the voltage per phase and increase the reliability and power density. Higher number of phases increase the torque-per-ampere ratio for the same machine volume and output power by widening the electrical conduction period. In this paper, the proposed seven-phase BLDC motor drive system is made into several functional modular blocks, so that it can be easily extended to other ac motor applications: back-EMF block, hysteresis current control block, pwm inverter block, phase current block, and speed/torque control block. Also in a system of BLDC motor drive, the PI controller has been widely used in the speed controller because of the simple implementation. To obtain a good speed response in a general drive system using the PI controller, the high bandwidth of a controller is established. therefore, in this paper, a Fuzzy controller is applied to the 7-phase BLDC motor drive system in order to improve the speed control performance. The Fuzzy controller is compared with a conventional PI controller through the experiment with respect to speed dynamic responses. These experimental results show that the Fuzzy controller of the 7-phase BLDC motor drive system is superior over the conventional PI controller. The algorithm using the Fuzzy controller can improve a comfortable ride in the field of high performance 7-phase BLDC motor drive applications.

• Journal of Electrical Engineering and Technology
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• 제12권5호
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• pp.1821-1834
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• 2017

Medium voltage control applications due to obtain better output voltage and reduced electro-magnetic interference multi level inverter is used. In closed loop control with inverter, the PI controller does not operate satisfactorily when the operating point changes. This paper presents the performance of Co-Efficient diagram PI controller based indirect vector controlled induction motor drive fed from three-level inverter under different operating conditions (dynamic and steady state). The proposed Co-Efficient diagram PI controller based three level inverter significantly reduces the torque ripple compared to that of conventional PI controller. The performance of the indirect vector controlled induction motor drive has been simulated at different operating conditions. For three-level inverter control, a simplified space vector modulation technique is implemented, which reduces the coordinate transformations complications in the algorithms. The performance parameters, torque ripple contents and THD of induction motor drive with three-level inverter is compared under different operating conditions using CDM-PI and conventional PI controllers.

• 한국조명전기설비학회:학술대회논문집
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• 한국조명전기설비학회 2007년도 추계학술대회 논문집
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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.