• Journal of Power Electronics
• /
• 제10권5호
• /
• pp.485-490
• /
• 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.

• 대한기계학회논문집A
• /
• 제28권3호
• /
• pp.238-244
• /
• 2004

To monitor the torque of an induction motor using current, the accurate identification of the motor parameters is very important. In this study, the motor parameters such as rotor resistance, stator and rotor leakage inductance, mutual inductance are estimated for torque monitoring and indirect vector control. Estimated parameters are used to monitor the torque of vector controlled induction motor without any speed measuring sensor. Stator current is measured to estimate the magnetizing current which is used to calculate flux linkage, rotor velocity and motor torque. From the experiments, the proposed method shows a good estimation of the motor parameters and torque under the normal rotational speed.

• KIEE International Transaction on Electrical Machinery and Energy Conversion Systems
• /
• 제5B권4호
• /
• pp.350-357
• /
• 2005

This paper presents a mechanical sensorless vector-controlled system with parameter identification by the aid of image processor. Based on the flux observer and the model reference adaptive system method, the proposed sensorless system includes rotor speed estimation and stator resistance identification using flux errors. Since the mathematical model of this system is constructed in a synchronously rotating reference frame, a linear model is easily derived for analyzing the system stability, including motor operating state and parameter variations. Because it is difficult to identify rotor resistance simultaneously while estimating rotor speed, a low-accuracy image processor is used to measure the mechanical axis position for calculating the rotor speed at a steady-state operation. The rotor resistance is identified by the error between the estimated speed using the estimated flux and the calculated speed using the image processor. Finally, the validity of this proposed system has been proven through experimentation.

• International Journal of Control, Automation, and Systems
• /
• 제3권1호
• /
• pp.79-86
• /
• 2005

In this paper, an estimator scheme for the rotor speed and flux of an induction motor is proposed on the basis of a fourth-order electrical model. It is assumed that only the stator currents and voltages are measurable, and that the stator currents are bounded. There are a number of common terms in the motor dynamics, and this is utilized to find a simple error model involving some auxiliary variables. Using this error model, the state estimation problem is converted into a parameter estimation problem assuming that the rotor speed is constant. Some stability properties are given on the basis of Lyapunov analysis. In addition, the rotor resistance, which varies with the motor temperature, can also be estimated within the same framework. The effectiveness of the proposed scheme is demonstrated through computer simulations and experiments.

• 한국정보통신학회:학술대회논문집
• /
• 한국해양정보통신학회 1998년도 춘계종합학술대회
• /
• pp.371-376
• /
• 1998

This paper describes an approach to a speed estimation method to remove speed sensor of underwater robot's AC drive systems. AC motors have been widely used in the field of underwater robot's manipulator or propulsion system. Most of these AC motors for underwater use have usually filled oil to compensate the high pressure in deep-sea operation, where a resolver is adopted to feed back the speed of rotor But this kind of speed feedback devices gives rise to some defects arising from their mechanical complexity and numerous signal lines; a resolver needs 6 or 7 signal lines for proper operation. This paper presents a speed estimation method to improve these problems of induction motor, which is adopted as a prototype of AC motor. The proposed speed estimation method is based on the RFO(rotor flux orientation) vector control method of voltage-fed AC drives. Using the controller of voltage-fed AC drives, it is unnecessary to measure the voltage for the estimation of rotor speed, which reduces the effects of measurement error Numerical simulation is carried out to investigate the validity of the method and the effects of rotors resistance variation.

• Journal of Power Electronics
• /
• 제9권4호
• /
• pp.654-666
• /
• 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.

• 한국마린엔지니어링학회:학술대회논문집
• /
• 한국마린엔지니어링학회 2005년도 후기학술대회논문집
• /
• pp.47-48
• /
• 2005

This paper describes how an Artificial Neural Network(ANN) can be employed to improve a speed estimation in a vector controlled induction motor drive. The system uses the ANN to estimate changes in the motor resistance, which enable the sensorless speed control method to work more accurately. Flux Observer is used for speed estimation in this system. Obviously the accuracy of the speed control of motor is dependent upon how well the parameters of the induction machine are known. These parameters vary with the operating conditions of the motor; both stator resistance(Rs) and rotor resistance(Rr) change with temperature, while the stator leakage inductance varies with load. This paper proposes a parameter compensation technique using artificial neural network for accurate speed estimation of induction motor and simulation results confirm the validity of the proposed scheme.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2001년도 학술대회 논문집 전문대학교육위원
• /
• pp.113-115
• /
• 2001

This paper presents the rotor parameter identification based on the MRAS theory and the speed estimation using ANN for the sensorless vector control of induction motor. The motor speed is estimated using ANN model which contains the rotor parameter. And the rotor parameter is identified using MRAS scheme which contains the rotor speed. The rotor speed estimate converges to its actual value as the rotor parameter error converges toward the zero. The simulation using Matlab/Simulink is performed to show the effectiveness of the proposed scheme.

• 대한전기학회논문지
• /
• 제38권11호
• /
• pp.877-887
• /
• 1989

In this paper, we attempt to achieve high dynamic performance by means of decoupled control of rotor speed and flux. Recently developed nonlinear feedback control theories are utilized. The rotor fluxes are estimated based on the rotor circuit equations. When the estimation error of the rotor flux tends to zero, the rotor speed and flux dynamic characteristics of the induction motor with our controller become linear. To minimize the deterioration of control performance, we use an identification algorithm for the rotor resistance. We analyze the dynamic behavior of the closed loop system with our controller. Both simulation and experimental results are included to demonstrate the practical significance of our result. In particular, our experimental results show that recently developed nonlinear feedback control techniques are of practical use in control of induction motors.

• Journal of Advanced Marine Engineering and Technology
• /
• 제30권1호
• /
• pp.169-175
• /
• 2006

This paper describes how an Artificial Neural Network(ANN) can be employed to improve a speed estimation in a vector controlled induction motor drive. The system uses the ANN to estimate changes in the motor resistance, which enable the sensorless speed control method to work more accurately. Flux Observer is used for speed estimation in this system. Obviously the accuracy of the speed control of motor is dependent upon how well the parameters of the induction machine are known. These parameters vary with the operating conditions of the motor; both stator resistance(Rs) and rotor resistance(Rr) change with temperature, while the stator leakage inductance varies with load. This paper proposes a parameter compensation technique using artificial neural network for accurate speed estimation of induction motor and simulation results confirm the validity of the proposed scheme.