• 대한전기학회논문지:전력기술부문A
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• 제48권3호
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• pp.286-293
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• 1999

A new control scheme using a torsional torque estimator based on a neural network is proposed and investigated for improving control characteristics of the high-performance motion control system. This control method presents better performance in the corresponding speed vibration response, compared with the disturbance observer-based control method. This result comes from the fact that the proposed neural network estimator keeps the self-learning capability, whereas the disturbance observer-based torque estimator with low pass filter should dbjust the time constant of the adopted filter according to the natural resonance frequency detemined by considering the system parameters varied. The simulation results shows the validity of the proposed control scheme.

• 전기학회논문지
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• 제60권2호
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• pp.293-300
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• 2011

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 and adaptive fuzzy learning contrroller(AFLC) for speed control in IPMSM Drives. AFLC is chaged fuzzy rule base by rule base modifier for robust control of IPMSM. 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 and AFLC is confirmed by comparing to conventional algorithm.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2001년도 Proceedings ICPE 01 2001 International Conference on Power Electronics
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• pp.443-447
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• 2001

In this paper, a robust sensorless vector control system for induction motors with a speed estimator and an uncertainty observer is presented. At first, the proposed speed estimator is based on the MRAS(Mode Reference Adaptive System) scheme and constructed with a simple fuzzy logic(FL) approach. The structure of the proposed FL estimator is very simple. The input of the FL is the rotor flux error difference between reference and adjustable model, and the output is the estimated incremental rotor speed Secondly, the unmodeled uncertainties such as parametric uncertainties and external load disturbances are modeled by a radial basis function network(RBFN). In the overal speed control system, the control inputs are composed with a norminal control input and a compensated control input, which are from RBFN observer output and the modeling error of the RBFN, repectively. The compensated control input is derived from Lyapunov unction approach. The simulation results are presented to show the validity of the proposed system.

• Journal of Electrical Engineering and Technology
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• 제2권2호
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• pp.241-248
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• 2007

The accuracy of all the schemes that belong to vector controlled induction machine drives is strongly affected by parameter variations. The aim of this paper is to examine iron losses and magnetic saturation effect in sensorless vector control of induction machines. At first, an approach to induction machine modelling and vector control scheme, which account for both iron loss and saturation, is presented. Then, a model reference adaptive system (MRAS) based speed estimator is developed. The speed estimation is modified in such a way that iron losses and the variation in the saturation level are compensated. Thus by substituting an artificial neural network flux estimator into the MRAS speed estimator. Experimental results are presented to verify the effectiveness of the proposed approach.

• 대한전기학회논문지:전력기술부문A
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• 제48권5호
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• pp.607-614
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• 1999

This paper deals with the design problem of the state estimator for servo system. The servo system has input time delay which depends on the computational time of control algorithm. The delayed input is a factor that brings out the state estimation error. So in order to reduce the state estimation error of the system, we propose a state estimator in which the delayed input of the system is considered. For this purpose, discrete time state space model is established accounting for the delayed input and a state estimator is designed based on this model. Kalman filter algorithm is employed in the design of the state estimator. The proposed estimator is used in the speed control of servo system with delayed input. Performance of the proposed state estimator is exemplified via simulations and experiments for servo system. Also, robustness of the proposed estimator to modeling error by variation of the system parameters is also shown in simulations.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1997년도 하계학술대회 논문집 F
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• pp.2129-2131
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• 1997

A sensorless controller of induction motor has several advantage: availability in a harsh environment. In this paper, the speed information is driving from the currents and the estimated stator flux. To obtain the estimated stator flux, this study is using The Estimator. The simulation results show that the proposed scheme has activity over a wide speed range and good response to load variations

• 한국정보통신학회논문지
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• 제5권1호
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• pp.150-156
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• 2001

본 논문에서는 센서리스 벡터제어를 위한 MRAC를 적용해 속도추정기를 설계한다. 이 속도추정기는 파라미터 변동 및 부하 외란에 강인하며, 그 속도값은 센서리스 벡터제어 구현을 위해 필요한 속도정보로 피드백되어 사용된다. 실험을 통해 그 타당성을 입증하였다.

• 전력전자학회:학술대회논문집
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• 전력전자학회 1998년도 연구회 합동 학술발표회 논문집
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• pp.23-27
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• 1998

This paper presents a new speed and position sensorless control method of permanent magnet synchronous motors based on the sliding mode observer. The sliding mode observer structure and its design method are discussed. Also, Lyapunov functions ar chosen for determining the adaptive law for the speed and the stator resistance estimator. The effectiveness of the proposed observer is confirmed by the computer simulation.

• 전력전자학회:학술대회논문집
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• 전력전자학회 1998년도 전력전자학술대회 논문집
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• pp.217-220
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• 1998

In this paper, for high performance as drive, in the speed sensorless vector control of induction motor, introduced flux estimator of voltage model and error compensation algorithm using closed loop integration method, and then we proposed a improved flux estimation method of high accuracy. And the rotor speed is estimating using the stator current and the estimated flux, it is used speed information. The proposed scheme is verified through digital simulations and experiments for 3.7[kW] induction motor and shows good dynamic performance.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.135-135
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• 2000

In this Paper. the DSP implementation of induction motor drive is Presented on the viewpoint of the design and experiment. The speed estimation of control system for induction motor drive is designed on the base of neural network speed estimator. This neural network speed estimator is experimentally applied to the induction motor system. This system Provides the satisfactory results.