• 조명전기설비학회논문지
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• 제18권1호
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• pp.85-89
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• 2004

산업자동화의 고정밀도에 따라 궤환 제어시스템은 강인한 제어가 요구되고 있다. 하지만 신경망 궤환 제어시스템이 외란의 영향을 받았을 때, 시스템의 강인한 제어는 어렵게 된다. 본 논문에서는 이러한 문제를 해결하기 위한 한 방법으로 신경회로망제어기와 PR제어기의 복합형 제어방법을 제시하였다. 신경회로망 제어기는 주 제어기로서 동작하고, PID제어기는 허용오차가 경계영역을 벗어날 때 동작하는 보조제어기로 사용된다. 신경회로망-PID복합형제어기의 강인성은 전동기의 속도제어에 의해서 확인하였다.

• 한국조명전기설비학회:학술대회논문집
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• 한국조명전기설비학회 2009년도 춘계학술대회 논문집
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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.

• 한국정보통신학회논문지
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• 제16권5호
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• pp.1095-1101
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• 2012

본 논문은 DSP 제어기와 IGBT 구동기를 이용하는 조립과 공급의 자동처리를 위한 BLDC 서보 모터 제어시스템 설계를 제안한다. 조립, 공급 자동처리 시스템은 다양한 동작을 위해 서보모터의 토크, 속도, 위치 제어를 필요로한다. 본 논문은 이러한 서보제어를 벡터제어와 공간벡터 PWM 기법을 이용하여 구현한다. 제어기의 CPU 로서 PWM 파형발생기, A/D 컨버터, SPI 통신 포트 및 많은 입/출력 포트를 갖는 TMS320F240 DSP를 채택하였다. 이 제어시스템은 메인 호스트 PC 가 위로부터의 명령을 전달하고 끝단의 서보제어기의 상태들을 모니터링하는 세 개의 부 DSP시스템을 관리하는 3레벨의 계층적 구조로 이루어져 있다. 각 부 DSP 시스템은 DSP와 IPM을 사용하여 BLDC 서보모터를 제어하는 8개의 BLDC 서보모터제어부를 운영한다. 호스트 시스템과 중간의 DSP는 RS-422을 이용하여 통신하며, 주프로세서와 제어기는 SPI 포트를 이용하여 통신한다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.2290-2295
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• 2005

In this paper, it is developed simulator system of 3 D.O.F parallel robot for educate of expertness. This simulator system is composed of three parts ? 3 D.O.F parallel robot, controller (hardware) and software. First, basic structure of the robot is 3 active rotary actuator that small geared step motor with fixed base. An input-link is connected to this actuator, and this input-link can connect two ball joints. Thus, two couplers can be connected to the input-link as a pair. An end-plate, which is jointed by a ball joint, can be connected to the opposite side of the coupler. A sub-link is produced and installed to the internal spring, and then this sub-link is connected to the upper and bottom side of the coupler in order to prevent a certain bending or deformation of the two couplers. The robot has the maximum diameter of 230 mm, 10 kg of weight (include the table), and maximum height of 300 mm. Hardware for control of the robot is composed of computer, micro controller, pulse generator, and motor driver. The PC used in the controller sends commands to the controller, and transform signals input by the user to the coordinate value of the robot by substituting it into equations of kinematics and inverse kinematics. A controller transfer the coordinate value calculated in the PC to a pulse generator by transforming it into signals. A pulse generator analyzes commands, which include the information received from the micro controller. A motor driver transfer the pulse received from the pulse generator to a step motor, and protects against the over-load of the motor Finally, software is a learning purposed control program, which presents the principle of a robot operation and actual implementation. The benefit of this program is that easy for a novice to use. Developed robot simulator system can be practically applied to understand the principle of parallel mechanism, motors, sensor, and various other parts.

• 전기학회논문지P
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• 제55권3호
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• pp.117-122
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• 2006

The paper is proposed artificial neural network(ANN) sensorless control of induction motor drive with fuzzy learning control-fuzzy neural network(FLC-FNN) 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 induction motor using FLC-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 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 proposed control algorithm is applied to induction motor drive system controlled FLC-FNN and ANN controller, Also, this paper is proposed the analysis results to verify the effectiveness of the FLC-FNN and ANN controller.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2011년도 제42회 하계학술대회
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• pp.1097-1098
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• 2011

This paper proposes high performance control of induction motor drive using fuzzy neural network precompensation PI(FNPPI) controller. To apply industrial processes, control methods is requested technique that can be demonstrate high performance and robust about load disturbance, parameter variation and uncertainty of model, etc. The PI controller dose not show satisfactory performance due to fixed gain. Therefore, this paper proposes FNPPI which is adjusted input values of PI controller according to operating conditions of motor by FNN controller mixed neural network and fuzzy. And this paper proves validity of proposed control algorithm through result analysis.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 춘계학술대회 논문집 전기기기 및 에너지변환시스템부문
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• pp.88-90
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• 2007

This paper is proposed high performance speed control using AFNN controller. The design of the speed controller based on adaptive fuzzy-neural networks(AFNN) controller that is implemented using fuzzy control and neural networks. The control performance of the AFNN controller is evaluated by analysis for various operating conditions. Analysis results are presented to show the validity of the proposed algorithm.

• 대한전기학회논문지:시스템및제어부문D
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• 제51권4호
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• pp.129-136
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• 2002

This paper presents a $H_{\infty}$ robust controller with parameter estimation in polynomial approach. For good performance of a uncertain system, the parameters are estimated by RLS algorithm. The controller minimizes the sum of $H_{\infty}$ norm between sensitivity function and complementary sensitivity function by employing the Youla parameterization and polynomial approach at the same time. A numerical example and its simulation results are given to show the validity of the proposed controller.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년 학술대회 논문집 정보 및 제어부문
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• pp.257-259
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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.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 심포지엄 논문집 정보 및 제어부문
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• pp.216-218
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• 2006

The paper is proposed high performance control of induction motor drive with adaptive fuzzy logic controller(AFLC). Also, this paper is proposed speed control of induction motor using AFLC 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 proposed control algorithm is applied to induction motor drive system controlled AFLC and ANN controller. And this paper is proposed the results to verify the effectiveness of the AFLC and ANN controller.