• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.190-190
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• 2000

This paper presents a multirate state feedback control (MRSFC) method for systems sensitive to disturbance and noise based on the multirate estimator design using the current estimator. MRSFC updates the controller output slower than the measurement sampling frequency of system output by a lifting factor R=T$\sub$c//T$\sub$s/. The closed-loop MRSFC system is less sensitive to disturbance and noise due to filtering effect than the conventional single-rate control system. The multirate estimator gain is obtained from solving a conventional pole placement problem such that MRSFC has the same spectrum of eigenvalues in the s-plane as the single-rate control. We applied the proposed multirate state feedback controller to a galvanometer servo system. Simulation and experimental results show that settling and tracking performances are improved compared with a conventional single-rate pole placement control (PPC).

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.339-341
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• 2006

This paper presents a new method on controller design of brushless dc motors. In such drives the current ripples are generated by motor inductance in stator windings and the back EMF. To suppress the current ripples the current controller is generally used. To minimize the size and the cost of the drives it is desirable to control motors without the current controller and the current sensing circuits. To estimate the motor current it is modeled by a neural network that is configured as an output-error dynamic system. The identified model is essentially a one step ahead prediction structure in which fast inputs and outputs are used to calculate the current output. Using the model, effective estimator to compensate the effects of disturbance has been designed. The effectiveness of the proposed current estimator is verified through experiments.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.12
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• pp.1004-1013
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• 2002

A multirate state feedback control (MRSFC) method is proposed for systems sensitive to disturbance and noise based on the multirate estimator design using current estimator. MRSFC updates the controller output slower than the measurement sampling fiequency of system output by a lifting factor $R=T_c/T_s$ The closed-loop MRSFC system is less sensitive to disturbance and noise due to filtering effect than the conventional single-rate control system The multirate estimator gain can be obtained by solving a conventional pole placement problem such that MRSFC has the same spectrum of eigenvalues in the s-plane as the single-rate control. We applied the proposed multirate state feedback controller to a galvanometer servo system Simulation and experimental results show that settling and tracking performances are improved compared with a conventional single-rate pole placement control (PPC).

• Journal of Korean Society for Quality Management
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• v.26 no.3
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• pp.71-81
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• 1998

In reliability analysis, the time difference between the expected next failure time and the current failure time or the Mean Time Between Failure(MTBF) is of significant interest. Until recently, in reliability growth studies, the reciprocal of the intensity function at current failure time has been used as being equal to MTBE($t_n$)at the n-th failure time $t_n$. That is MTBF($t_n$)=l/$\lambda (t_n)$. However, such a relationship is only true for Homogeneous Poisson Process(HPP). Tsokos(1995) obtained the upper bound and lower bound for the MTBF($t_n$) and proposed an estimator for the MTBF($t_n$) as the mean of the two bounds. In this paper, we provide the estimator for the MTBF($t_n$) which does not depend on the value of the shape parameter. The result of the Monte Carlo simulation shows that the proposed estimator has better efficiency than Tsokos's estimator.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.52 no.2
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• pp.87-92
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• 2003

This paper presents a flux estimator for the sensorless vector control of induction motors. The proposed method utilize the combination of the voltage model based on stator equivalent model and the current model based on rotor equivalent model, which enables stable estimation of rotor flux in high speed region and in low speed region. The dynamic performance of proposed method is verified through the experiment. The experimental results show that motors ran easily start even under 150[%] load condition and operate continuously below 0.5[Hz].

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2374-2376
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• 2000

In this paper, we propose an indirect vector control method using Adaptive Neuro-Fuzzy Inference System (ANFIS) parameter estimator. It estimates the rotor time constant when the indirect vector control of induction motor is applied. We use the stator current error that is difference between the current command and estimated current calculated from terminal voltage and current. And two induced current estimate equations are used in training ANFIS.The estimator is trained by the hybrid learning algorithm. Simulation results shows good performance under load disturbance and motor parameter variations.

• Proceedings of the KIPE Conference
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• 2007.11a
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• pp.7-10
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• 2007

The current model based sensorless method has many benefits that it can be robust control for large load torque. However, this method should determine a coefficient of back electro motive force(back-emf). This coefficient is varied by load torque and speed. Also, the coefficient determining equation is not exist, so it is determined only by experiment. On the other hands, using only back-emf estimatior method can not drive in low speed area and it has weakness in load variation. For these problems, this paper suggests the hybrid sensorless method that mixes the back-emf estimator regarding saliency and the current based sensorless model. This estimator offers not only non-necessary coefficient for current sensorless model, but also wide speed area operating in no specific transition method.

• The Transactions of the Korean Institute of Power Electronics
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• v.18 no.6
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• pp.587-592
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• 2013

In this paper, the performance improvement of the sensorless control of IPMSM employing the active flux concept by the improved current estimator is presented. The accuracy of the current estimator used in a previous report is degraded when the motor parameters are not known exactly. A simple current estimator derived from estimated flux is proposed to improve the position estimation performance. In order to show the usefulness of the proposed estimation method, the simulation results using Matlab/Simulink and the experiment results are presented.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.660-662
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• 2004

This paper proposes an Intelligent Speed Estimator in order to realize the speed-sensorless vector control of an induction motor. Intelligent Speed Estimator used Model Reference Adaptive System which has Fuzzy-Neural adaptive mechanism as Speed Estimation method. The Intelligent Speed Estimator estimates the speed of an induction motor with a rotor flux of a reference model and adjustable model in MRAS. The Intelligent Speed Estimator reduces the error of the rotor flux between the voltage flux model and the current flux model using the error and the change of error as input of the Estimator. The computer simulation is executed to verify the propriety and the effectiveness of the proposed speed estimator.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.12
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• pp.1865-1874
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• 2013

In this paper, a design method of speed and position estimator based on unscented Kalman filter is proposed for the no sensor control of IPMSM(Interior Permanent Magnet Synchronous Motor). The proposed method is simple more than the estimator designed with rotation axis for current measurement. Also the proposed state estimator is designed including nonlinear terms of the estimator. The controller which constructed using nonlinear back-stepping control method is operated speed and current control using the estimated speed and currents information. Through simulation, the performance of the designed estimator is compared to the estimator which is designed to synchronize d-q axis.