• Journal of Electrical Engineering and Technology
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• v.2 no.2
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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.

• Proceedings of the KIEE Conference
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• 2007.10a
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• pp.67-69
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• 2007

PMSM (Permanent Magnet Synchronous Motor) current control is a most inner loop of electromechanical driving systems and it plays a foundation role in the hierarchy's control loop of several mechanical machine systems. In this paper, a simple RMRAC control scheme for the PMSM is proposed in the synchronous frame. In the synchronous current model, the input signal is composed of as a calculated voltage by adaptive laws and system disturbances. The gains of feed-forward and feed-back controller are estimated by the proposed e-modification methods respectively, where the disturbances are assumed as filtered current tracking errors. After the estimation of the disturbances from the tracking errors, the corresponding voltage is fed forward to control input to compensate for the disturbances. The proposed method is robust to high frequency disturbances and has a fast dynamic response to time varying reference current trajectory. It also shows a good real-time performance duo to it's simplicity of control structure. Through the simulations considering several cases of external disturbances and experimental results, efficiency of the proposed method is verified

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.49 no.9
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• pp.626-634
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• 2000

The paper describes a new and rigorous mathematical model using counter-EMF for the rotor field oriented system with induction motor which uses the estimated speed and rotor flux based on a Model Reference Adaptive System as well as the real-time approach. The estimated speed and rotor flux is used for the speed and flux feedback control. The stability and the convergence of the estimator are improved on the basis of hyperstability theory for non-linear systems. The validity of the proposed method is verified by simulation and also the sensorless control was tested on the propulsion system simulator used for the development of Korean High-Speed Railway Train(KHSRT).

• Proceedings of the KIEE Conference
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• 2000.07b
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• pp.1136-1139
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• 2000

The paper describes a new and rigorous mathematical model for the rotor field oriented system with induction motor which uses the estimated speed and rotor flux based on a Model Reference Adaptive System, as well as the real-time approach. The estimated speed and rotor flux is used for the speed and flux feedback control. The stability and the convergence of the estimator are improved on the basis of hyperstability theory for non-linear systems. The real-time controller and estimator are implemented with a sampling period of $926{\mu}s$ using a dual TMS320C44 floating-point digital signal processor. The validity of the proposed method is verified by simulation, and also, the sensorless control was tested on the propulsion system simulator, used for the development of Korean High-Speed Railway Train (KHSRT) [5].

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.3
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• pp.584-593
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• 2018

The highly efficient operation of induction motors has been studied in the past years. Among the many attempts made to obtain highly efficient operation, Maximum Torque Per Amp (MTPA) controls in induction motor drives were proposed. This method enables induction motor drives to operate very efficiently since it achieves the desired torque with the minimal stator current. This is because the alternate qd induction motor model (AQDM) is a highly accurate mathematical model to represent the dynamic characteristics of induction motors. However, it has been shown that the variation of the rotor resistance degrades the performance of the MTPA control significantly, thus leading to its failure to satisfy the maximum torque per amp condition. To take into consideration the mismatch between the actual value of the rotor resistance and its parameter value in the design of the control strategy, an adaptive MTPA control was proposed. In this work, this adaptive MTPA control is investigated in order to achieve the desired torque with the minimum stator current at multiple operating points. The experimental study showed that (i) the desired torque was accurately achieved even though there was a deviation of the order of 5% from the commanded torque value at a torque reference of 25 Nm (tracking performance), and (ii) the minimum stator current for the desired torque (maximum torque per amp condition) was consistently satisfied at multiple operating points, as the rotor temperature increased.

• Proceedings of the KIEE Conference
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• 2007.04c
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• pp.71-73
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• 2007

Speed and torque controls of induction motors are usually attained by the application of position and speed sensors. However, speed and position sensors require the additional mounting space, reduce the reliability in harsh environments and increase the cost of a motor. Therefore, many studies have been performed for the elimination of speed and position sensors. This paper investigates an improved sensorless control of an induction motor. The proposed control strategy utilizes the MRAS(Model Reference Adaptive System) for estimating the speed of a sensorless induction motor. The proposed algorithm is verified through the simulation and experimentation.

• Proceedings of the KIPE Conference
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• 2002.07a
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• pp.213-216
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• 2002

This paper describes a new approach to estimate induction motor speed from terminal voltages and currents for speed-sensorless vector control. This algorithm is based on Model Reference Adaptive System(MRAS). The proposed technique is simple and robust to the variation of motor parameters. Specially, this algorithm is not affected by the variation of stator resistance and it does not require any pure integration at all. The validity of this new approach is proved by simulations.

• Proceedings of the KIPE Conference
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• 2003.11a
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• pp.186-189
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• 2003

This paper is proposed to position and speed control of interior permanent magnet synchronous motor(IPMSM) drive without mechanical sensor. A gopinath observer is used for the mechanical state estimation of the motor. The observer was developed based on nonlinear model of IPMSM, that employs a d-q rotating reference frame attached to the rotor, A gopinath observer is implemented to compute the speed and position feedback signal. The validity of the proposed scheme is confirmed by various response characteristics.

• Journal of Electrical Engineering and information Science
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• v.2 no.6
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• pp.20-27
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• 1997

We present a method of electromyographic(EMG) pattern recognition to identify motion commands for the control of a prosthetic arm by evidence accumulation with multiple parameters. Integral absolute value, variance, autoregressive(AR) model coefficients, linear cepstrum coefficients, and adaptive cepstrum vector are extracted as feature parameters from several time segments of the EMG signals. Pattern recognition is carried out through the evidence accumulation procedure using the distances measured with reference parameters. A fuzzy mapping function is designed to transform the distances for the application of the evidence accumulation method. Results are presented to support the feasibility of the suggested approach for EMG pattern recognition.

• 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.