• 전기학회논문지P
• /
• 제54권1호
• /
• pp.8-14
• /
• 2005

This paper is proposed hybrid artificial intelligent(HAI) controller for speed control of surface permanent magnet synchronous motor(SPMSM) drive. The design of this algorithm based on HAI controller that is implemented using fuzzy control and neural network. This controller uses fuzzy rule as training patterns of a neural network. Also, this controller uses the back-propagation method to adjust the weights between the neurons of neural network in order to minimize the error between the command output and actual output. A model reference adaptive scheme is proposed in which the adaptation mechanism is executed by fuzzy logic based on the error and change of error measured between the motor speed and output of a reference model. The control performance of the HAI controller is evaluated by analysis for various operating conditions. The results of analysis prove that the proposed control system has strong high performance and robustness to parameter variation, and steady-state accuracy and transient response.

• 대한전기학회논문지:전기기기및에너지변환시스템부문B
• /
• 제53권9호
• /
• pp.569-575
• /
• 2004

This paper is proposed adaptive fuzzy-neural network(FNN) controller for high performance of induction motor drive. The design of this algorithm based on FNN controller that is implemented using fuzzy control and neural network. This controller uses fuzzy rule as training patterns of a neural network. Also, this controller uses the back-propagation method to adjust the weights between the neurons of neural network in order to minimize the error between the command output and actual output. A model reference adaptive scheme is proposed in which the adaptation mechanism is executed by fuzzy logic based on the error and change of error measured between the motor speed and output of a reference model. The control Performance of the adaptive FNN controller is evaluated by analysis for various operating conditions. The results of analysis prove that the proposed control system has strong high performance and robustness to parameter variation. and steady- state accuracy and transient response.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2005년도 학술대회 논문집 정보 및 제어부문
• /
• pp.570-572
• /
• 2005

This paper is proposed adaptive hybrid artificial intelligent(HAI) controller for high performance of induction motor drive. The design of this algorithm based on fuzzy-neural network(FNN) controller that is implemented using fuzzy control and neural network. This controller uses fuzzy rule as training patterns of a neural network. Also, this controller uses the back-propagation method to adjust the weights between the neurons of neural network in order to minimize the error between the command output and actual output. A model reference adaptive scheme is proposed in which the adaptation mechanism is executed by fuzzy logic based on the error and change of error measured between the motor speed and output of a reference model. The control performance of the adaptive FNN controller is evaluated by analysis for various operating conditions. The results of experiment prove that the proposed control system has strong high performance and robustness to parameter variation, and steady-state accuracy and transient response.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2002년도 하계학술대회 논문집 D
• /
• pp.2120-2122
• /
• 2002

There are several types of control systems that use fuzzy logic controller as a essential system component. The majority of research work on fuzzy PID controller focuses on the conventional two-input PI or PD type controller. However, fuzzy PID controller design is a complex task due to the involvement of a large number of parameters in defining the fuzzy rule base. In this paper we combined conventional PI type and PD type fuzzy controller and set the initial parameters of this controller from the conventional PID controller gains obtained by Ziegler-Nichols tuning or other coarse tuning methods. After that, by replacing some of these parameters with sing1e neurons and making them to be adjusted by back-propagation learning algorithm we designed a neuro-fuzzy controller which showed good performance characteristics in both computer simulation and actual application.

• 정보과학회지
• /
• 제18권8호
• /
• pp.78-89
• /
• 2000

Today's computers are built up from a minimal set of standard pattern recognition operations. Logic gates, such as NAND, are common examples. Biomolecular materials offer an alternative approach, both in terms of variety and context sensitivity. Enzymes, the basic switching elements in biological cells, are notable for their ability to discriminate specific molecules in a complex background and to do so in a manner that is sensitive to particular milieu features and indifferent to others, The enzyme, in effect, is a powerful context sensitivity pattern processor that in a rough way can be analogized to a neuron whose input-output behavior is controlled by enzymatic dynamics.

• 대한전기학회논문지:시스템및제어부문D
• /
• 제55권4호
• /
• pp.154-157
• /
• 2006

This paper is proposed hybrid artificial intelligent(HAI) controller for high performance of induction motor drive. The design..of this algorithm based on fuzzy-neural network(FNN) controller that is implemented using fuzzy control and neural network. This controller uses fuzzy rule as training patterns of a neural network. Also, this controller uses the back-propagation method to adjust the weights between the neurons of neural network in order to minimize the error between the command output and actual output. A model reference adaptive scheme is proposed in which the adaptation mechanism is executed by fuzzy logic based on the error and change of error measured between the motor speed and output of a reference model. The control performance of the adaptive FNN controller is evaluated by analysis for various operating conditions. The results of experiment prove that the proposed control system has strong high performance and robustness to parameter variation, and steady-state accuracy and transient response.

• 조명전기설비학회논문지
• /
• 제28권9호
• /
• pp.1-7
• /
• 2014

Brushless Direct Current(BLDC) Motor is essential to measure a rotor position because of that this motor type needs to synchronize the rotor's position and changeover phase current instead of a brush and commutator used on the existing dc motor. Recently, many researches have studied on sensorless control drive for BLDC motor. The conventional control methods are a compensation value dq, Kalman filter, Fuzzy logic, Neurons neural network, and the like. These methods has difficulties of detecting BEMF accurately at low speed because of low BEMF voltage and switching noise. And also, the operation is long and complex. So, it is required a high-performance microprocessor. Therefore, it is not suitable for a small BLDC motor sensorless drive. This paper presents control methods suitable for economic small BLDC motor sensorless drive which are an improved design of the BEMF detection circuit, simplifying a complex algorithm and computation time reduction. The improved motor sensorless drive is verified stability and validity through being designed, manufactured and analyzed.

• 전자공학회논문지SC
• /
• 제41권3호
• /
• pp.39-46
• /
• 2004

본 논문은 IPMSM 드라이브의 고성능 속도 제어를 위하여 퍼지제어와 신경회로망을 혼합 구성한 적응 FNN 제어기를 제시한다. 적응 FNN 제어기는 기준 모델에 기초한 적응 메카니즘을 적용하여 신경회로망의 고도의 적응제어와 퍼지제어기의 강인성 제어의 장점들을 접목한다. 적응 FNN 제어기의 출력은 FNN 제어기의 출력과 적응 퍼지제어의 출력을 합하여 출력을 얻는다. 적응 FNN 제어기는 다양한 동작조건에서 응답특성을 분석하고 평가한다. 제시한 적응 FNN 제어기의 타당성은 IPMSM 드라이브 시스템에 적용하여 성능 결과로 입증한다.

• 한국조명전기설비학회:학술대회논문집
• /
• 한국조명전기설비학회 2005년도 학술대회 논문집
• /
• pp.315-320
• /
• 2005

This paper is proposed adaptive fuzzy-neuro controller for high performance of induction motor drive. The design of this algorithm based on fuzzy-neural network controller that is implemented using fuzzy control and neural network. This controller uses fuzzy rule as training patterns of a neural network. Also, this controller uses the back-propagation method to adjust the weights between the neurons of neural network in order to minimize the error between the command output and actual output. A model reference adaptive scheme is proposed in which the adaptation mechanism is executed by fuzzy logic based on the error and change of nor measured between the motor speed and output of a reference model. The control performance of the adaptive fuzy-neuro controller is evaluated by analysis for various operating conditions. The results of experiment prove that the proposed control system has strong high performance and robustness to parameter variation, and steady-state accuracy and transient response.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2004년도 하계학술대회 논문집 B
• /
• pp.1248-1250
• /
• 2004

This paper is proposed adaptive fuzzy-neural network controller(NFC) for speed control of surface permanent magnet synchronous motor(SPMSM) drive. The design of this algorithm based on NFC that is implemented using fuzzy control and neural network. This controller uses fuzzy rule as training patterns of a neural network. Also, this controller uses the back-propagation method to adjust the weights between the neurons of neural network in order to minimize the error between the command output and actual output. A model reference adaptive scheme is proposed in which the adaptation mechanism is executed by fuzzy logic based on the error and change of error measured between the motor speed and output of a reference model. The control performance of the adaptive NFC is evaluated by analysis for various operating conditions. The results of analysis prove that the proposed control system has strong high performance and robustness to parameter variation, and steady-state accuracy and transient response.