• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.2
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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.

• The Transactions of the Korean Institute of Power Electronics
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• v.3 no.3
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• pp.206-213
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• 1998

In this paper, an adaptive compensation technique for parameter variation is proposed which can perform quick torque response in vector control of an induction motors. To solve the problem of control performance degradation due to parameter variation in an induction motor, a rotor resistance estimation is performed by the model reference adaptive control(MRAC). The algorithm of rotor resistance estimation is composed of the error relationship which is generated between a motor real instantaneous reactive power and an estimated instantaneous reactive power. The advantage of such a real reactive power reference model is independence of the motor parameter variation. The estimation rotor resistance values are applied to the direct vector control system with a flux observer. Finally, the simulations and experiment are presented to validate the rotor resistance estimation algorithm of induction motor.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.6
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• pp.174-180
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• 2016

This paper study about an unscented Kalman filter with a variety type of unscented transformation to estimate state values for speed control without position sensor of a permanent-magnet synchronous motor. The principles of an unscented transformation and unscented Kalman filter are examined and their application is explained. Generally the mapping process can be divided into two type, such as a basic and a general form according to a scaling parameter. And computation time, the number of samples, and weights about samples are different from each other. But, there is no little information on the scaling parameter value how this value influences the system performance. Simulation and experimental results show the validity of the designed unscented transformation performance with the various scaling parameter values for sensorless motor drive.

• Journal of the Korean Institute of Telematics and Electronics T
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• v.35T no.2
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• pp.56-65
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• 1998

In this paper, a neural network(NN) control system is proposed and practically implemented, which is adequate to the induction motor speed control system with frequent load variation. The back propagation neural network technique is used to provide a real adaptive estimation of the motor parameter. The error between the desired state variable and the actual one is back-propagated to adjust the motor parameter, so that the actual state variable will coincide with the desired one. Designed control system is based on PC-DSP structure for the purposed of easiness of applying NN algorithm. Through computer simulation and experimental results, it is verified that proposed control system is robust to the load variation and practical implementation is possible.

• Proceedings of the KIEE Conference
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• 2003.04a
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• pp.248-252
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• 2003

This paper has a control method proposed for the effective self-tuning fuzzy speed control based on neural network of the induction motor indirect vector control. The vector control of an induction motor provides the decoupled control of the rotor flux magnitude and the torque producing current to performance is desirable. But, the drive performance often degrades for the machine parameter variations and its condition give rise to coupling of flux and torque current. The fuzzy speed control of an induction motor has the robustness about machine parameter variations compared with conventional PID speed control in a way. That proved to be some waf from the true. The purpose of this paper is to improve the adaptation by offering self-turning function to fuzzy speed controller. In this paper, the adaptive mechanism of fuzzy speed control in used ANN(Artificial Neural Network) technique is applied in an IFO induction machine drive, such that the machine can follow a reference model (an ideal field oriented machine) to achieve desired speed. In this paper proved the self-turning method of fuzzy controller has the robustness about parameter variation and the wide range of adaptation by simulation.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.4
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• pp.542-548
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• 2012

This study presents a new method of system modeling by using the Discrete Fourier Transform for the position control system of DC Motor. And the proposed method is similar to the method of System Identification by analysis of correlation of the measured input-output data. The measured output signals are transformed to the frequency domain using DFT. The Fourier Spectrum of the transformed signals is used for knowing to the feature of having an important effect on the system. And transfer function of the second order system is estimated by the dominant parameter which is computed in the magnitude and the phase of Fourier spectrum of the transformed signals. In addition, the output signal includes the unique feature of system. So, although the basic parameter of the system is unknown for us, the proposed method has an advantage to system modeling. And the controller is easily designed by the estimated transfer function. Thus, in this paper, the proposed method is applied to the system modeling for the position control system of DC Motor and the PD-controller is designed by the estimated model. And the efficiency and the reliability of the proposed method are verified by the experimental result.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.708-713
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• 1994

A new flux observer based vector control system of an induction motor is constructed by using an observer in which the commanded stator currents are used to estimate the rotor flux. In this system, the flux observer is formulated by using a model of induction motor in a stationary coordinate system. By considering an observer of induction motor in a fixed co-ordinate system located on its secondary flux, a slip frequency controlled type of vector control system is also proposed. From these control schemes, the relation between the conventional slip frequency controlled type system and the observer based one is clarified. The steady-state error of the developed torque which is caused by the parameter change of induction motor is analyzed and discussed for the selection of observer gains. The poles of the observer error dynamics and those of the observer based vector control system are calculated analytically by neglecting the machine parameter change. In order to analyze the robust stability, a linear model of the observer based vector control system is proposed taking into account the machine parameter change. By using this model, the trajectories of the poles and zeros of the torque transfer function are computed and discussed. To test validity of the theoretical analysis, experiments are conducted by using a digital signal processor (TMS320C30) and a current controlled voltage source PWM inverter.

• The Transactions of the Korean Institute of Power Electronics
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• v.23 no.1
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• pp.66-71
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• 2018

We propose an online parameter estimation method for surface-mounted permanent-magnet synchronous motor (SPMSM) using an affine projection algorithm (APA). The proposed method estimates parameters with two APAs based on the discrete-time model equation of SPMSM during motor operation. The first APA is designed to estimate inductance, and the second APA is designed to estimate resistance and flux linkage. However, in case when the d-axis current is controlled to 0A, the second APA cannot estimate resistance and flux linkage simultaneously because the matrix rank in APA becomes deficient. To overcome this problem, we temporarily inject a negative reference current input to the d-axis control loop, and the matrix in the APA then becomes full rank, which enables the simultaneous estimation of resistance and flux linkage. The proposed method is verified by PSIM simulation and an actual experiment, and the results reveal that SPMSM parameters can be estimated online during motor operation.

• The Transactions of the Korean Institute of Power Electronics
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• v.15 no.6
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• pp.425-431
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• 2010

This paper proposes a new adaptive speed controller for the speed control of a surface-permanent magnet synchronous motor. The proposed adaptive controller is very insensitive to model parameter and load torque variations since it does not require any accurate information on the motor parameter and load torque values. Moreover, the stability of the proposed control system is analytically proven. To verify the effectiveness of the proposed adaptive speed controller, simulation and experimental results are shown under motor parameter and load torque variations. It is clearly validated that the proposed speed regulator can precisely control the speed of permanent magnet synchronous motors.

• The Transactions of the Korean Institute of Power Electronics
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• v.9 no.5
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• pp.413-419
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• 2004

In the induction motor direct vector control system using the Gopinath model flux observer, the deterioration of the dynamic response due to the detuned rotor time constant is investigated. To solve this problem, the on line estimation algorithm of the rotor time constant using model reference adaptive control is proposed. The effect of the motor parameter variation on the rotor time constant estimation is analyzed through experiment. The estimation error due to the parameter variation converges within 5%. Thus applying the proposed algorithm to the Gopinath model flux observer, the robust direct vector control system of the induction motor to the parameter variation can be implemented.