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MRAS 퍼지제어를 이용한 유도전동기 회전자의 시정수 추정
김길봉(Kil-Bong Kim),최정식(Jung-Sik Choi),고재섭(Jae-Sub Ko),정동화(Dong-Hwa Chung) 한국조명·전기설비학회 2006 한국조명·전기설비학회 학술대회논문집 Vol.2006 No.11월
This paper presents time constant estimation of induction motor using MRAS(model reference adaptive system) fuzzy control. The rotor time constant is enabled from the estimation of rotor flux, which has two method. One is to estimate it based on the stator current and the other is to integrate motor terminal voltage. if the parameters are correct, these two methods must yield the same results. But, For the case where the rotor time constant is over or under estimated, the two rotor flux estimation have different angles. Furthermore their angular positions are related to the polarity of rotor time constant estimation error. Based on these observation, this paper develops a rotor time constant update algorithm using fuzzy control. This paper shows the theoretical analysis as well as the simulation results to verify the effectiveness of the new method.
김길봉(Kil-Bong Kim),최정식(Jung-Sik Choi),고재섭(Jae-Sub Ko),정동화(Dong-Hwa Chung) 대한전기학회 2006 대한전기학회 학술대회 논문집 Vol.2006 No.10
This paper proposes a new method of on-line estimation for rotor resistance of the induction motor in the indirect vector controlled drive, using artificial neural network (ANN). The back propagation algorithm is used for training of the neural networks. The error between the desired state variable of an induction motor and actual state variable of a neural network model is back propagated to adjust the weight of a neural network model, so that the actual state variable tracks the desired value. The performance of rotor resistance estimator and torque and nux responses of drive, together with these estimators, are investigated variations rotor resistance from their nominal values. The rotor resistance are estimated analytically, using the proposed ANN in a vector controlled induction motor drive.
신경회로망을 이용한 IPMSM 드라이브의 온라인 파라미터 추정
고재섭(Jae-Sub Ko),최정식(Jung-Sik Choi),김길봉(Kil-Bong Kim),정동화(Dong-Hwa Chung) 한국조명·전기설비학회 2006 한국조명·전기설비학회 학술대회논문집 Vol.2006 No.11월
A number of techniques have been developed for estimation of speed or position in motor drives. The accuracy of these techniques is affected by the variation of motor parameters such as the stator resistance, stator inductance or torque constant. This paper is proposed a neural network based estimator for torque and stator resistance in IPMSM Drives. The neural weights are initially chosen randomly and a model reference algorithm adjusts those weights to give the optimum estimations. The neural network estimator is able to track the varying parameters quite accurately at different speeds with consistent performance. The neural network parameter estimator has been applied to slot and flux linkage torque ripple minimization of the IPMSM. The validity of the proposed parameter estimator is confirmed by the operating characteristics controlled by neural networks control.
최정식(Jung-Sik Choi),고재섭(Jae-Sub Ko),김길봉(Kil-Bong Kim),정동화(Dong-Hwa Chung) 한국조명·전기설비학회 2006 한국조명·전기설비학회 학술대회논문집 Vol.2006 No.11월
This paper is proposed genetic algorithm tuning(GAT) controller for high performance of induction motor drive. We employed GA to the classical PI controller. The approach having ability for global optimization and with good robustness, is expected to overcome some weakness of conventional approaches and to be more acceptable for industrial practices. The control performance of the GAT PI 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.
고재섭(Jae-Sub Ko),최정식(Jung-Sik Choi),김길봉(Kil-Bong Kim),정동화(Dong-Hwa Chung) 대한전기학회 2006 대한전기학회 학술대회 논문집 Vol.2006 No.10
This paper presents self tuning PI controller of IPMSM drive using neural network. NNPI controller is developed to minimize overshoot, rise time and settling time following sudden parameter changes such as speed, load torque and inertia. Also, this paper is proposed speed control of IPMSM using neural network and estimation of speed using artificial neural network(ANN) controller. The results on a speed controller of IPMSM are presented to show the effectiveness of the proposed gain tuner. And this controller is better than the fixed gains one in terms of robustness, even under great variations of operating conditions and load disturbance.
신경회로망을 이용한 IPMSM 드라이브의 효율 최적화 제어
최정식(Jung-Sik Choi),고재섭(Jae-Sub Ko),김길봉(Kil-Bong Kim),정동화(Dong-Hwa Chung) 전력전자학회 2006 전력전자학술대회 논문집 Vol.- No.-
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 F-NN controller. Also, this paper proposes speed control of IPMSM using F-NN 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 F-NN controller, the operating characteristics controlled by efficiency.
ALM-FNN 제어기에 의한 SynRM의 효율 최적화 제어
최정식(Jung-Sik Choi),고재섭(Jae-Sub Ko),김길봉(Kil-Bong Kim),정동화(Dong-Hwa Chung) 대한전기학회 2006 대한전기학회 학술대회 논문집 Vol.2006 No.10
This paper is proposed an efficiency optimization control algorithm for a synchronous reluctance motor which minimizes the copper and iron losses. The design of the speed controller based on adaptive learning mechanism-fuzzy neural networks (ALM-FNN) controller that is implemented using adaptive, fuzzy control and neural networks. The control performance of the hybrid artificial intelligent controller is evaluated by analysis for various operating conditions. Analysis results are presented to show the validity of the proposed algorithm
Hybrid-PI 제어기를 이용한 유도전동기의 고성능 제어
최정식(Jung-Sik Choi),고재섭(Jae-Sub Ko),김길봉(Kil-Bong Kim),정동화(Dong-Hwa Chung) 대한전기학회 2006 대한전기학회 학술대회 논문집 Vol.2006 No.10
This paper presents Hybrid-PI controller of induction motor drive using fuzzy control. In general, PI controller in computer numerically controlled machine process fixed gain. They may perform well under some operating conditions, but not all. To increase the robustness, fixed gain PI controller, Hybrid-PI controller proposes a new method based self tuning PI controller. Hybrid-PI controller is developed to minimize overshoot and settling time following sudden parameter changes such as speed, load torque, inertia, rotor resistance and self inductance. The results on a speed controller of induction motor are presented to show the effectiveness of the proposed gain tuner. And this controller is better than the fixed gains one in terms of robustness, even under great variations of operating conditions and load disturbance.
퍼지적응 메카니즘을 이용한 IPMSM의 HBPI 제어기
고재섭(Jae-Sub Ko),최정식(Jung-Sik Choi),김길봉(Kil-Bong Kim),정동화(Dong-Hwa Chung) 한국조명·전기설비학회 2006 한국조명·전기설비학회 학술대회논문집 Vol.2006 No.11월
This paper presents Hybrid PI(HBPI) controller of IPMSM drive using fuzzy adaptive mechanism control. In general, PI controller in computer numerically controlled machine process fixed gain. They may perform well under some operating conditions, but not all. To increase the robustness, fixed gain PI controller, HBPI controller proposes a new method based self tuning PI controller. HBPI controller is developed to minimize overshoot and settling time following sudden parameter changes such as speed, load torque, inertia, rotor resistance and self inductance. The results on a speed controller of IPMSM are presented to show the effectiveness of the proposed gain tuner. And this controller is better than the fixed gains one in terms of robustness, even under great variations of operating conditions and load disturbance.
인공 신경회로망 센서리스에 의한 SynRM의 효율 최적화 제어
최정식(Jung-Sik Choi),고재섭(Jae-Sub Ko),김길봉(Kil-Bong Kim),정동화(Dong-Hwa Chung) 한국조명·전기설비학회 2006 한국조명·전기설비학회 학술대회논문집 Vol.2006 No.11월
This paper is proposed an efficiency optimization control algorithm for a synchronous reluctance motor(SynRM) which minimizes the copper and iron losses. ALso, this paper presents a sensorless control scheme of SynRM using artificial neural network(ANN). There exists a variety of combinations of d and q -axis current which provide a specific motor torque. The objective of the efficiency optimization controller is to seek a combination of d and q -axis current components, which provides minimum losses at a certain operating point in steady state. It is shown that the current components which directly govern the torque production have been very well regulated by the efficiency optimization control scheme. The proposed algorithm allows the electromagnetic losses in variable speed and torque drives to be reduced while keeping good torque control dynamics. The control performance of ANN is evaluated by analysis for various operating conditions. Analysis results are presented to show the validity of the proposed algorithm.