• 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.

• 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.10 no.2
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• pp.186-193
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• 2005

This paper presents a strategy to drive built in-type spindle induction motor which is used as CNC(Computer Numerical Control) in the industry. Gopinath model flux estimator which is composed of current model to be profitable in the low speed range and voltage model to be profitable in the high speed range is used for rotor flux estimation. Moreover this paper presents to drive the spindle motor in the high speed range by using the flux weakening control. High speed operation of spindle motor in the field weakening region is verified through simulations and experiments.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.1
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• pp.37-44
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• 2021

This study is implemented as a sensor-less position controller capable of multi-turn detection to replace the expensive absolute encoder used in the turret servo system. For sensor-less control, the position information of the rotor is essential. For this, a magnetic flux estimator was implemented from the mathematical model of IPMSM used in the turret servo system. The position of the rotor and the angular velocity of the rotor were obtained using the rotor magnetic flux calculated from the magnetic flux estimator. Using the zero-crossing technique, one pulse was generated for each rotation of the estimated rotor magnetic flux to measure the number of multi-turns. Simulation and experiment results confirmed the usefulness of the proposed method.

• The Transactions of the Korean Institute of Power Electronics
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• v.18 no.3
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• pp.232-239
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• 2013

This paper proposes an improved stator flux estimator through ensuring conventional PLPF to act as a pure integrator for sensorless control of induction motors. Conventional PLPF uses the estimated synchronous speed as a cut-off frequency and has the gain and phase compensators. The gain and phase compensators are determined on the assumption that the estimated synchronous angular speed is coincident with the real speed. Therefore, if the synchronous angular speed is not same as the real speed, the gain and phase compensation will not be appropriate. To overcome the problem of conventional PLPF, this paper analyzes the relationship between the synchronous speed error and the phase lag error of the stator flux. Based on the analysis, this paper proposes the synchronous speed error compensation scheme. To achieve a start-up without speed sensor, the current model is used as the stator flux estimator at the standstill. When the motor starts up, the current model should be switched into the voltage model. So a stable transition between the voltage model and the current model is required. This paper proposes the simple transition method which determines the initial values of the voltage model and the current model at the transition moment. The validity of the proposed schemes is proved through the simulation results and the experimental results.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.27 no.4
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• pp.245-271
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• 2023

In this paper we discuss some recovery of H(div)-conforming flux approximations from the equilibrated fluxes of Ainsworth and Oden for quadratic finite element methods of second-order elliptic problems. Combined with the hypercircle method of Prager and Synge, these flux approximations lead to a posteriori error estimators which provide guaranteed upper bounds on the numerical error. Furthermore, we prove some superconvergence results for the flux approximations and asymptotic exactness for the error estimator under proper conditions on the triangulation and the exact solution. The results extend those of the previous paper for linear finite element methods.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.10
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• pp.1442-1447
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• 2012

In this paper, direct power control system for three-phase PWM AC/DC converter without the source voltage sensors is proposed. The sinusoidal input current and unity effective power factor are realised based on the estimated flux in the observer. Both active and reactive power calculated using estimated flux. The estimation of flux is performed based on the Reduced-order flux observer using the actual currents and the command control voltage. The source voltage sensors are replaced by a flux estimator. The active and reactive powers estimation are performed based on the estimated flux and Phase anble. The proposed algorithm is verified through simulation and experiment.

• Proceedings of the KIEE Conference
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• 2003.10b
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• pp.197-199
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• 2003

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.