• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.5 no.3
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• pp.117-126
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• 2012

This study configuration Vector Control System which is stable and has outstanding Dynamic Characteristics in Very Low Speed Region and Low Speed Region, and proposes Instantaneous Speed Observer and Very Low Speed Control method using Reduced-Dimensional State Observer. The Observer proposed in this system, by appling Reduced-Dimensional State Observer to Load-Torque estimation and using for speed estimation, implements system composition simply and is capable of accurate Instantaneous Speed estimation in Very Low Speed Region. Also, this study reduces influence by System Noise and suggests an induction motor speed control system which is effective in Load Disturbance, modeling error, estimation noise and so on without changing pole of an Observer.

• 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.

• Proceedings of the KIEE Conference
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• 2004.07d
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• pp.2304-2306
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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.

• International Journal of Automotive Technology
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• v.6 no.5
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• pp.475-481
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• 2005

This paper deals with the algorithm of estimating the longitudinal speed of a braking vehicle using measurements from an accelerometer and a standard wheel speed sensor. We evolve speed estimation algorithms of increasing complexity and accuracy on the basis of experimental tests. A final speed estimation algorithm based on a Kalman filtering is developed to reduce measurement noise of the wheel speed sensor, error of the tire radius, and accelerometer bias. This developed algorithm can give peak errors of less than 3 percent even when the accelerometer signal is significantly biased.

• Transactions of the Korean Society of Automotive Engineers
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• v.10 no.6
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• pp.234-240
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• 2002

This paper treats the problem of estimating the longitudinal velocity of a braking vehicle using measurements from an accelerometer and wheel speed data from standard anti-lock braking wheel speed sensors. We develop and experimentally test three velocity estimation algorithms of increasing complexity. The algorithm that works the best gives peak errors of less than 3 percent even when the accelerometer signal is significantly biased.

• Proceedings of the KIEE Conference
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• 1998.07a
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• pp.9-11
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• 1998

Most of sensorless algorithms are based on motor equations including electrical and mechanical parameters. However, parameter variation and uncertain error decrease the accuracy of speed estimation of PMSM. This paper investigates the sensorless speed control of PMSM considering parameter variation. The proposed algorithm use the speed compensator which is robust in parameter variation and error. The simulation and experimental results indicate good performances.

• Proceedings of the KIPE Conference
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• 2002.11a
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• pp.3-6
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• 2002

In a conventional speed sensorless stator flux-oriented(SFO) induction motor drive system, when the estimated speed is transformed into the sample-data model using the first-forward difference approximation, the sampled data model has a modeling error which, in turn, produces an error in the rotor speed estimation. The error included in the estimated speed is removed by the use of a low pass filter (LPF). As the result, the delay of the estimated speed occurs in transients by the use of the LPF This paper investigates the problem of a conventional speed sensorless SFO system due to the delay of estimated speed in the filed weakening region. In addition, this paper proposes a method to estimate exactly speed by using Luenberger observer, The proposed method is verified by experiment with a 5-hp induction motor drive.

• Journal of Power Electronics
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• v.16 no.6
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• pp.2150-2161
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• 2016

Mechanical and electrical parameter uncertainties cause dynamic and static estimation errors of the rotor speed and position, resulting in performance deterioration of sensorless control systems. This paper applies an extended nonlinear observer to interior permanent magnet synchronous motors (IPMSM) for the simultaneous estimation of the rotor speed and position. Two compensation methods are proposed to improve the observer performance against parameter uncertainties: an on-line rotational inertia adjustment approach that employs the gradient descent algorithm to suppress dynamic estimation errors, and an equivalent flux error compensation approach to eliminate static estimation errors caused by inaccurate electrical parameters. The effectiveness of the proposed control strategy is demonstrated by experimental tests.

• International Journal of Control, Automation, and Systems
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• v.3 no.1
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• pp.79-86
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• 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.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.10
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• pp.690-696
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• 2004

This paper is proposed a hybrid intelligent controller based on the vector controlled surface permanent magnet synchronous motor(SPMSM) drive system. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also, this paper is proposed speed control of SPMSM using neural network-fuzzy(NNF) control and speed estimation using artificial neural network(ANN) Controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed, so that the actual state variable will coincide with the desired one. The back propagation mechanism is easy to derive and the estimated speed tracks precisely the actual motor speed. This paper is proposed the theoretical analysis as well as the simulation results to verify the effectiveness of the new method.