• Proceedings of the KIPE Conference
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• 2001.07a
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• pp.430-433
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• 2001

When the vector control, which does not need a speed signal from a mechanical speed sensor, it is possible to reduce the cost of the control equipment and to improve the control performance in many industrial application. This paper describes a rotor speed identification method of induction motor based on the theory of flux model reference adaptive system. The estimator execute the rotor speed identification so that the vector control of the induction motor may be achieved. The improved auxiliary variable of the two model are introduced In perform accurate rotor speed estimation. The control system is composed of the PI controller for speed control and current controller using space voltage vector PWM technique. High speed calculation and processing for vector control is carried out by TMS320C31 digital signal processor. Validity of the proposed control method is verified through simulation and experimental result.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.454-457
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• 2004

In this paper, control method proposed for effective speed control of the induction motor indirect vector control. For the induction motor drive, indirect vector control scheme that controls torque current and flux current of the stator current independently so that it can have improved dynamics. Also, neuro-fuzzy algorithm employed for torque current control in order to optimal speed control The proposed neuro-fuzzy algorithm can be applied to the precise speed control of an induction motor drive system or the field of any other power systems.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1478-1481
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• 2005

As the model of most practical system cannot be obtained, the practice of typical control method is limited. Accordingly, numerous artificial intelligence control methods have been used widely. Fuzzy control and neural network control have been an important point in the developing process of the field. This paper is proposed adaptive fuzzy-neural network based on the vector controlled interior permanent magnet synchronous motor drive system. The fuzzy-neural network is first utilized for the speed control. A model reference adaptive scheme is then proposed in which the adaptation mechanism is executed using fuzzy-neural network. Also, this paper is proposed estimation of speed of interior permanent magnet synchronous motor using artificial neural network 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 analysis results to verify the effectiveness of the new method.

• Proceedings of the KIPE Conference
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• 2018.07a
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• pp.57-59
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• 2018

This paper presents a novel reference variable speed control scheme of a BLDC motor for the high-speed blender machine according to the current limit. Because of a pulsating load variation of a high-speed blender machine, the actual speed is pulsated by the current limit in the high-speed region. The proposed control scheme uses a variable reference speed to reduce the speed variation from the current limit in the constant power region. The pulsated load is occurred at the material crushing, then the pulsated load is reduced after grinding. The reference speed is smoothly reduced at the pulsated load variation, then the enough torque can make a constant speed during crushing. When the pulsating load is reduced, the reference speed is automatically increased to the original speed value. The proposed control scheme is verified by experimental result by practical blender machine.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.754-758
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• 1996

In this paper, a new method of position synchronizing control is proposed for multi-axes driving system. The proposed synchronizing control system is constituted with speed and synchronizing controller. The structure of synchronizing control system is varied by sign of synchronizing error. When a disturbance input becomes added to one axis, this axis becomes slave axis. The other axis is master axis. Therefore, master axis is not influenced by the disturbance. The speed controller of the first axis is designed by $H_{\infty}$ control theory. The speed controller of the second axis is designed by inverse dynamics of speed control system of the first axis. The speed control system designed with $H_{\infty}$ controller guarantees low sensitivity for the disturbance as well as robustness against model uncertainties. Especially, the synchronizing controller is designed to keep position error to minimize by controlling speed of slave axis. The effectiveness of the proposed method is successfully confirmed through several experiments.

• Transactions of the Korean Society of Automotive Engineers
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• v.2 no.5
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• pp.23-29
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• 1994

The design procedure for fuzzy logic controller depends on the expert's knowledge or trial and error. Moreover, it is very difficult to guarantee the stability and robustness of the system due to the linguistic expression of fuzzy control. However, fuzzy logic control has succeeded in many control problems that the conventional control theory has difficulties to deal with. As a result, this control theory is applied to the engine control system which a mathematical model is difficult. In this study, the fuzzy logic is applied to obtain the gain of PI control at idle speed control system, and a simple engine model is developed in order to perform simulation. Experimental results show that the response to reach the target engine speed at idle speed control system is improved by adopting the gain obtained with fuzzy logic.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.282-284
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• 2006

Generally PI controller is used to control the speed of an induction motor. It has the good performance of speed control in case of adjusting the control parameters. But it occurred the problem to change the control parameters in the change of operation condition. In order to solve this problem, Fuzzy control or Artificial neural network is introduced in the speed control of an induction motor. However, Fuzzy control have the problems as the difficulties to change the membership function and fuzzy rule and the remaining error. Also Neural network has the problem as the difficulties to analyze the behavior of inner part. Therefore, the study on the combination of two controller is proceeded. In this paper, Speed controller of an induction motor based fuzzy-neural network is proposed and the speed control of an induction motor is performed using the proposed controller. Through the experiment, the fast response and good stability of the proposed speed controller is proved.

• Journal of Advanced Navigation Technology
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• v.24 no.2
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• pp.101-106
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• 2020

In order to smoothly control the speed of the induction motor, it is necessary to obtain the required rotor speed information. In order to obtain the speed information, it must be obtained using a sensor, but it can also be obtained using an appropriate algorithm without using a sensor. In order to obtain speed information, a system was designed using a model reference adaptive system (MARS). Indirect vector control, one of the speed control methods of induction motors, was calculated from the motor current and rotor parameter values. The method of obtaining the position information of the magnetic flux by combining the slip frequency with the rotor speed was used. It is possible to simply perform instantaneous current control in a wide speed range without actual magnetic flux information, and has the advantage that the structure of the controller is simple. Therefore, in this paper, the control system was constructed based on the indirect vector control method, and the speed control system of the induction motor was developed by estimating the required rotor speed information as an intelligent algorithm developed without using it as a sensor.

• Proceedings of the KIPE Conference
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• 1997.07a
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• pp.177-181
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• 1997

In the speed control system of motors using the low resolution rotary encoder, the period of encoder pulse becomes longer than the sampling time for speed control in the range of very low speed. Therefore, it is difficult to obtain accurate speed information. In this paper, the speed estimating method at the very low speed region using reduced order torque observer, which has been widely used, is examined. The results of simulation show that the characteristics of the speed control at the very low speed region is improved by using the reduced order torque observer.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.9
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• pp.692-699
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• 2003

A novel rotor speed estimation method using Model Reference Adaptive Control(MRAC) is proposed to improve the performance of a sensorless vector controller. In the proposed mettled, the stator current is used as the model variable for estimating the speed. In conventional MRAC methods, the relation between the two model errors and the speed estmation error is unclear. Yet, in the proposed method, the stator current error is represented as a function of the first degree for the error value in the speed estimation. Therefore, the proposed method can produce a fast speed estimation and is robust to the parameters error In addition, the proposed method of offers a considerable improvement in the performance of a sensorless vector controller at a low speed. The superiority of the proposed method is verified by simulation and experiment in a low speed region and at a zero-speed.