• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2158-2160
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• 2002

In this paper, we designed the adaptive fuzzy controller(AFLC) using neural network and tabu search. We tuned the weights of neural network changing adaptively input/output gain of fuzzy logic controller and the gain of fuzzy logic controller using tabu search. To evaluate the proposed method's effectiveness, we apply the proposed AFLC to the speed control of an actual AC servomotor system. The experimental results show that AFLC has the better control performance than PI controller in terms of settling time, rising time and overshoot.

• Journal of the Korean Institute of Intelligent Systems
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• v.4 no.1
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• pp.35-49
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• 1994

It is well known that, for nominimum phase systems, a conventional linear controller of PID type or an adaptive controller of this structure shows limitation in achieving a satisfactory performance under tight specifications. In this paper, we combine a neuro-controller with a PI-controller with off-line learning capability provided by the Genetic Algorithm to propose a novel neuro-controller to control nonminimum phase systems effectively. The simulation results show that our proposed model is more efficient with faster rising time and less undershoot effect when the performances of the proposed controller and a conventional form are compared.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.38 no.11
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• pp.888-895
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• 1989

A novel model reference adaptive control (MRAC) scheme for electrical servo drives is proposed, in which the control input is synthesized without any parameter identification mechanism and a PI controller is inserted ahead of the plant to reduce the steady state chattering. The proposed scheme is shown to be asymptotically stable in the case where the load torque disturbance satisfies a certan condition. An application to a permanent magnet synchronous motor drive shows that the output error between the plant and the reference model tends to zero and the chattering is greatly reduced.

• Proceedings of the KIEE Conference
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• 2003.07b
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• pp.1128-1131
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• 2003

This paper proposes a speed control system based on a fuzzy logic approach, integrated with a simple and effective adaptive algorithms. And this paper attempts to provide a thorough comparative insight into the behavior of induction motor drive with PI, direct and improved fuzzy speed controller. A indirect vector controlled induction motor is simulated under varying operating condition. The validity of the comparative results is confirmed by simulation results for induction motor drive system.

• Journal of Biosystems Engineering
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• v.15 no.2
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• pp.110-122
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• 1990

This study was undertaken to develop the feed rate control system for the head feed thresher by making use of the microprocessor and to evaluate the response of the system to a various threshing conditions. The control unit was composed of one-board microcomputer. The speed of the wet-paddy feeding chain was controlled by dc moter with PI controller. It was used the adaptive control method to maintain the constant feed rate regardless of the fed rice varieties. The sliding type potentiometer was used as the feed rate sensor, which was attached on the sheaf-holding apparatus. The mathematical models of the system components were derived and computer simulation was developed for investigating the parameters affecting on control performance and for estimating the response of the system. A one-board microcomputer-based feed rate control system developed in this study was properly functioned and assessed as adequate for the feed rate control system of the head feed thresher. Based on the simulation for the bundle feed, it was anticipated that the lower setting value of the cylinder speed(RL) is to be set higher than the limiting operational speed. In addition, the higher setting value of the cylinder speed(RH) is to be set lower than the limiting cylinder speed for threshing. The computer simulation for the continuous spread feed showed that the lower the setting value of straw layer thickness(LL) was set, the shorter the correction time. However, if too low LL may be established, the feed rate could not reach to its desired rate.

• Journal of Navigation and Port Research
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• v.32 no.5
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• pp.379-385
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• 2008

In this paper, we presents a scheme for the parameter estimation and optimal control scheme for apparatus of container crane system. For parameter estimation, first, we construct the open loop of the container crane system and estimate its parameters based on input-output data, a real-coded genetic algorithm(RCGA) and the model adjustment technique. The RCGA plays an important role in parameter estimation as an adaptive mechanism. For controller design, state feedback gain matrix is searched by another RCGA and the estimated model. The performance of the proposed methods are demonstrated through a set of simulation and experiments of the experimental apparatus.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.58 no.3
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• pp.249-256
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• 2009

The widely used control theory based design of PI family controllers fails to perform satisfactorily under parameter variation, nonlinear or load disturbance. In high performance applications, it is useful to automatically extract the complex relation that represent the drive behaviour. The use of learning through example algorithms can be a powerful tool for automatic modelling variable speed drives. They can automatically extract a functional relationship representative of the drive behavior. These methods present some advantages over the classical ones since they do not rely on the precise knowledge of mathematical models and parameters. The paper proposes high performance speed and current control of synchronous reluctance motor(SynRM) drive using adaptive learning mechanism-fuzzy neural network (ALM-FNN) and fuzzy logic control (FLC) controller. The proposed controller is developed to ensure accurate speed and current control of SynRM drive under system disturbances and estimation of speed using artificial neural network(ANN) controller. Also, this paper proposes the analysis results to verify the effectiveness of the ALM-FNN, FLC and ANN controller.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.25 no.7
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• pp.15-25
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• 2011

This proposes an online loss minimization algorithm for series PI(SPI) based interior permanent magnet synchronous motor(IPMSM) drive to yield high efficiency and high dynamic performance over wide speed range. The loss minimization algorithm is developed based on the motor model. In order to minimize the controllable electrical losses of the motor and thereby maximize the operating efficiency, the d-axis armature current is controlled optimally according to the operating speed and load conditions. For vector control purpose, a SPI is used as a speed controller which enables the utilization of the reluctance torque to achieve high dynamic performance as well as to operate the motor over a wide speed range. Also, this paper proposes current control of model reference adaptive fuzzy controller(MFC), and estimation of speed using artificial neural network(ANN) controller. The proposed efficiency optimization control, SPI, MFC, ANN in this paper is applied to IPMSM drive system, the validity of this paper is proved by analyzing response characteristics in variety operating conditions.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2009.05a
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• pp.416-419
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• 2009

The widely used control theory based design of PI family controllers fails to perform satisfactorily under-parameter variation, nonlinear or load disturbance. In high performance applications, it is useful to automatically extract the complex relation that represent the drive behaviour. The use of loaming through example algorithms can be a powerful tool for automatic modelling variable speed drives. They can automatically extract a functional relationship representative of the drive behavior. These methods present some advantages over the classical ones since they do not rely on the precise knowledge of mathematical models and parameters. The paper proposes high performance speed and current control of synchronous reluctance motor(SynRM) drive using adaptive loaming mechanism-fuzzy neural network (ALM-FNN) and fuzzy logic control(FLC) controller. The proposed controller is developed to ensure accurate speed and current control of SynRM drive under system disturbances and estimation of speed using artificial neural network(ANN) controller. Also, this paper proposes the analysis results to verify the effectiveness of the ALM-FNN and ANN controller.

• The Transactions of the Korean Institute of Power Electronics
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• v.10 no.3
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• pp.282-289
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• 2005

This paper proposes a fuzzy neural network controller based on the vector control for interior permanent magnet synchronous motor(IPMSM) drive system. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability This paper does not oかy presents speed control of IPMSM using neuro-fuzzy control(NFC) but also 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. Thus, it is presented the theoretical analysis as well as the analysis results to verify the effectiveness of the proposed method in this paper.