• Proceedings of the KIEE Conference
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• 2003.10b
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• pp.79-82
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• 2003

This paper presents a new speed and position sensorless of Switched Reluctance Motor(SRM) based on the sliding mode observer. The sliding mode observer structure and its design method are discussed. Also, Lyapunov functions are chosen for determining the speed and the stator resistance estimator. The effectiveness of the proposed observer system is confirmed by the computer simulation.

• Journal of Power Electronics
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• v.10 no.5
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• pp.485-490
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• 2010

Sensorless control methods are generally used in motor control for home-appliances because of the material cost and manufactureing standard restrictions. The current model-based control algorithm is mainly used for PMSM sensorless control in the home-appliance industry. In this control method, the rotor position is estimated by using the d-axis and q-axis current errors between the real system and a motor model of the position estimator. As a result, the accuracy of the motor model parameters are critical in this control method. A mismatch of the PMSM parameters affects the speed and torque in low speed, steadystate responses. Rotor position errors are mainly caused by a mismatch of the stator resistance. In this paper, a stator resistance compensation algorithm is proposed to improve sensorless control performance. This algorithm is easy to implement and does not require a modification of the motor model or any special interruptions of the controller. The effectiveness of the proposed algorithm is verified through experimental results.

• Proceedings of the KIPE Conference
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• 2000.07a
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• pp.421-424
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• 2000

Direct Torque Control(DTC) of AC motor has the fast torque and flux dynamic responses even though it has very simple scheme to implement. However DTC do not show good performance at low speed range with conventional open loop stator flux observer when stator resistance varied. So authors proposed nonlinear stator flux obsever in order to flux estimation at low speed and show its simulation results.

• 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.

• Proceedings of the KIEE Conference
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• 2006.07b
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• pp.761-762
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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 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.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2371-2373
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• 2000

This paper presents a new vector control scheme for induction motor. An exact knowledge of the rotor flux position is essential for a high-performance vector control. The position of the rotor flux is measured in the direct scheme and estimated in the indirect schemes. Since the estimation of the flux position requires a priori knowledge of the induction motor parameters, the indirect schemes are machine parameter dependent. The rotor and stator resistance among the parameters change with temperature. Variations in the parameters of induction machine cause deterioration of both the steady state and dynamic operation of the induction motor drive. Several methods have presented to minimize the consequences of parameter sensitivity in indirect scheme. In this paper, new estimation scheme of rotor flux position is presented to eliminate sensitivity due to variation in the resistance. The simulation is executed to verify the proposed vector control performance and to compare its performance with that of indirect vector control.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.3
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• pp.145-153
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• 2004

A nonlinear adaptive speed observer is designed for the sensorless control of induction motors. In order to design the speed observer, the measurements of the stator currents and the estimates of the rotor fluxes are used. The sliding mode cascade observer is designed to estimate the time derivatives of the stator currents. The open-loop observer is designed to estimate the rotor fluxes and its time derivatives using the stator current derivatives. The adaptive observer is also designed to estimate the rotor resistance. Sequentially, the rotor speed is calculated using these estimated values. It is shown that the estimation errors of the corresponding states and the parameters converge to the specified residual set. It is also shown that the speed controller using these estimates is performed well. The simulation examples are represented to investigate the validity of the proposed observers for the sensorless control of induction motors.

• Journal of Power Electronics
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• v.9 no.4
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• pp.654-666
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• 2009

Direct torque control (DTC) of induction machines (IM) is a well-known strategy of these drives control which has a fast dynamic and a good tracking response. In this paper a nonlinear DTC of speed sensorless IM drives is presented which is based on input-output feedback linearization control theory. The IM model includes iron losses using a speed dependent shunt resistance which is determined through some effective experiments. A stator flux vector is estimated through a simple integrator based on stator voltage equations in the stationary frame. A novel method is introduced for DC offset compensation which is a major problem of AC machines, especially at low speeds. Rotor speed is also determined using a rotor flux sliding-mode (SM) observer which is capable of rotor flux space vector and rotor speed simultaneous estimation. In addition, stator and rotor resistances are estimated using a simple but effective recursive least squares (RLS) method combined with the so-called SM observer. The proposed control idea is experimentally implemented in real time using a FPGA board synchronized with a personal computer (PC). Simulation and experimental results are presented to show the capability and validity of the proposed control method.

• Proceedings of the KIPE Conference
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• 1998.07a
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• pp.239-244
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• 1998

We propose a nonlinear feedback controller that can control the induction motors with high dynamic performance by means of decoupling of motor speed and rotor flux. A new recursive adaptation algorithm for rotor resistance which can be applied to our nonlinear feedback controller is also presented in this paper. Some simulation results show that the adaptation algorithm for rotor resistance is robust against the variation of stator resistance and mutual inductance. In addition, it is computationally simple and has small estimation errors.