• The Transactions of the Korean Institute of Electrical Engineers B
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• v.53 no.5
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• pp.332-337
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• 2004

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.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.5
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• pp.429-433
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• 2007

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 ststor 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.

• Proceedings of the KIEE Conference
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• 2006.04a
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• pp.207-209
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• 2006

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 ststor 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.

• KIEE International Transaction on Electrical Machinery and Energy Conversion Systems
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• v.3B no.2
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• pp.97-102
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• 2003

In this paper, a recurrent artificial neural network (RNN) based self-tuning speed controller is proposed for the high-performance drives of induction motors. The RNN provides a nonlinear modeling of a motor drive system and could provide the controller with information regarding the load variation system noise, and parameter variation of the induction motor through the on-line estimated weights of the corresponding RNN. Thus, the proposed self-tuning controller can change the gains of the controller according to system conditions. The gain is composed with the weights of the RNN. For the on-line estimation of the RNN weights, an extended Kalman filter (EKF) algorithm is used. A self-tuning controller is designed that is adequate for the speed control of the induction motor The availability of the proposed controller is verified through MATLAB simulations and is compared with the conventional PI controller.

• Proceedings of the KIEE Conference
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• 2001.07e
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• pp.108-112
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• 2001

Vector control schemes are used in inverter-fed induction motor drives to obtain high performance. Crucial to the success of the vector control scheme is the knowledge of the instantaneous position of the rotor flux. However, the position of the rotor flux change with temperature and magnetic saturation of the motor. This variation cause deterioration of both steady state and dynamic operation of the motor drives. Performance degradation is in the form of input-output torque nonlinearity and saturation of the motor. Analytic expressions are derived to evaluate the effects due to parameter sensitivity.

• 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 Electrical Engineers P
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• v.53 no.2
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• pp.51-57
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• 2004

Vector control schemes are used in inverter-fed induction motor drives to obtain high performance. Crucial to the success of the vector control scheme is the knowledge of the instantaneous position of the rotor flux. However, the position of the rotor flux change with temperature and magnetic saturation of the motor. This variation cause deterioration of both steady state and dynamic operation of the motor drives. Performance degradation is in the form of input-output torque nonlinearity and saturation of the motor. Analytic expressions are derived to evaluate the effects due to parameter sensitivity. Also, dynamic response is shown by speed command with the variation of stator and rotor resistance.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.1
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• pp.151-158
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• 2003

Design changes frequently occur while design activities are performed. If the impact of design changes is estimated, design efficiency can be improved. But, the types of design changes are various and they can affect other design parts. Hence, it is difficult to deal with design changes directly. The purpose of this research is to develop systematic algorithms for change propagation tracing and change impact analysis, and then to implement a change impact analysis system. We have selected a process-based design and a design environment which is composed of design parameters and constraints. The algorithm for change propagation tracing tracks the change propagation of design parameters and finds design parameters, constraints and tasks which are probably changed. In the algorithm for change impact analysis, a change impact value is calculated from the list of changeable tasks. These two algorithms have been implemented into change impact analysis system (CIAS). CIAS has been applied to the redesign of 2 stage gear drives. CIAS can improve the efficiency of design activities. If there are many alternatives for a design change at the redesign step, designers can calculate the change impact value of each alternative and perform design change activities in the direction of minimizing design change impact.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.806-807
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• 2011

This paper presents a new sensorless controller used with both the classical sliding mode observer(SMO) and the rate of current change in order to a reduced torque ripple for switched reluctance motor (SRM) sensorless drives. The new sensorless scheme consists of a sliding mode observer (SMO)-based position sensorless approach for high speeds along with a low-resolution discrete the rate of current change for low speeds and standstill. The new position estimation resets between the SMO and the low-resolution of current change according to the speed sign and the position error difference between the SMO and the low-resolution rate of current change. The simulation results show the robustness of this new high performance sensorless control approach with the hybrid sensorless control topology.

• KIEE International Transaction on Electrical Machinery and Energy Conversion Systems
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• v.5B no.2
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• pp.162-167
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• 2005

This paper deals with the method of characteristic analysis of the capacitor-run single- phase induction motor having two poles (4-pole and 2-pole). This motor, which is referred to as a pole change motor in this paper, is capable of variable speed operation without inverters or drives. However, speed-torque curve can be distorted by the harmonic components contained in the magnetic flux density distribution. Therefore, the characteristics of this motor are analyzed using equivalent circuit considering harmonic components and the simulation results are compared with the experimental results.