• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.704-707
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• 1997

In this paper, we present a fuzzy logic based tuner for continuous on-line tuning of PID controllers. The essential idea of the scheme is to parameterize a Ziegler-Nichols-like tuning formula by a singler parameter .alpha., then to use an on line fuzzy logic to self-tune the parameter. The adaptive scaling makes the controller robust against large variations in parametric and dynamics uncertainties in the plant model. New self-tuning controller has the ability to decide when to use PI or PID control by extracting process dynamics from relay experiments. These scheme lead to improved performance of the transient and steady state behavior of the closed loop system, including processes with nonminimum phase processes.

• Proceedings of the KIPE Conference
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• 2001.10a
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• pp.443-447
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• 2001

In this paper, a robust sensorless vector control system for induction motors with a speed estimator and an uncertainty observer is presented. At first, the proposed speed estimator is based on the MRAS(Mode Reference Adaptive System) scheme and constructed with a simple fuzzy logic(FL) approach. The structure of the proposed FL estimator is very simple. The input of the FL is the rotor flux error difference between reference and adjustable model, and the output is the estimated incremental rotor speed Secondly, the unmodeled uncertainties such as parametric uncertainties and external load disturbances are modeled by a radial basis function network(RBFN). In the overal speed control system, the control inputs are composed with a norminal control input and a compensated control input, which are from RBFN observer output and the modeling error of the RBFN, repectively. The compensated control input is derived from Lyapunov unction approach. The simulation results are presented to show the validity of the proposed system.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.05a
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• pp.420-424
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• 2006

본 논문에서는 외란을 갖는 불확실한 비선형 시스템을 제어하기 위하여 $H^{\infty}$ 제어를 이용한 강인 간접 적응 퍼지 관측기를 설계하여 상태변수를 관측하고 외란관측기를 설계하여 시스템의 외란을 제거하는 강인한 제어기를 구성한다. 제안된 외란관측기는 시스템과 외란의 대역폭보다 큰 궤환 이득을 가짐으로써 기존의 역플랜트 모델 또는 퍼지 기반의 외란관측기 보다 간단한 구조를 가지고 외란과 시스템 모델링 오차의 합을 관측해 낼 수 있다. 본 논문에서는 도립진자 시스템의 모의실험을 통하여 관측기, 외란관측기와 제어기의 성능을 평가한다.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.2
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• pp.125-130
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• 2001

This paper presents an indirect field-oriented (IFO) induction motor position servo drives which uses the model following adaptive controller with the artificial neural network(ANN)-based rotor resistance estimator. The model reference adaptive system(MRAS)-based 2-layer ANN estimates the rotor resistance on-line and a linear model-following position controller is designed by using the estimated the rotor resistance value. At the end, a fuzzy logic system(FLS) is added to make the position controller robust to the external disturbances and the parameter variations. The simulation results show the effectiveness of the proposed method.

• Journal of Power Electronics
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• v.15 no.3
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• pp.730-740
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• 2015

This paper presents model reference adaptive system speed estimators based on Type-1 and Type-2 fuzzy logic controllers for the speed sensorless direct torque and flux control of an induction motor drive (IMD) using space vector pulse width modulation. A Type-1 fuzzy logic controller (T1FLC) based adaptation mechanism scheme is initially presented to achieve high performance sensorless drive in both transient as well as in steady-state conditions. However, the Type-1 fuzzy sets are certain and cannot work effectively when a higher degree of uncertainties occurs in the system, which can be caused by sudden changes in speed or different load disturbances and, process noise. Therefore, a new Type-2 FLC (T2FLC) - based adaptation mechanism scheme is proposed to better handle the higher degree of uncertainties, improve the performance, and is also robust to different load torque and sudden changes in speed conditions. The detailed performance of different adaptation mechanism schemes are performed in a MATLAB/Simulink environment with a speed sensor and sensorless modes of operation when an IMD is operates under different operating conditions, such as no-load, load, and sudden changes in speed. To validate the different control approaches, the system is also implemented on a real-time system, and adequate results are reported for its validation.

• Proceedings of the KIEE Conference
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• 1995.11a
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• pp.293-295
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• 1995

In this article a fuzzy controller and neural network adaptive observer is proposed and applied to the case of induction motor control. The proposed observer which comprises neural network flux observer and neural network torque observer is trained to learn the flux dynamics and torque dynamics and subjected to further on-line training by means of a backpropagation algorithm. Therefore it has been shown that the robust control of induction motor neglects the rotor time constant variations.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.19 no.8
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• pp.33-39
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• 2005

The maximum output torque and power developed by the machine is ultimately depended on the allowable inverter current rating and maximum voltage which the inverter can supply to the machine. Therefore, considering the limited voltage and current capacities, it is desirable to consider a control method which yields the best possible torque per ampere. In this paper, we propose fuzzy neural network(FNN) controller that combines a fuzzy control and the neural network for high performance control of induction motor drive. This controller composes antecedence of the fuzzy rules and consequence by a clustering method and a multi-layer neural networks. This controller is compounding of advantages that robust control of a fuzzy control and high-adaptive control of the neural networks. Also, this paper is proposed control of maximum torque per ampere(MTPA) of induction moor. This strategy is reposed which is simple in structure and has the honest goal of minimizing the stator current magnitude for given load torque. The performance of the proposed induction motor drive with maximum torque control using FNN controller is verified by analysis results at dynamic operation conditions.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.15 no.3
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• pp.186-191
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• 2015

Hand gesture is the most common tool used to interact with and control various electronic devices. In this paper, we propose a novel hand gesture recognition method using fuzzy logic based classification with a new type of sensor array. In some cases, feature patterns of hand gesture signals cannot be uniquely distinguished and recognized when people perform the same gesture in different ways. Moreover, differences in the hand shape and skeletal articulation of the arm influence to the process. Manifold features were extracted, and efficient features, which make gestures distinguishable, were selected. However, there exist similar feature patterns across different hand gestures, and fuzzy logic is applied to classify them. Fuzzy rules are defined based on the many feature patterns of the input signal. An adaptive neural fuzzy inference system was used to generate fuzzy rules automatically for classifying hand gestures using low number of feature patterns as input. In addition, emotion expression was conducted after the hand gesture recognition for resultant human-robot interaction. Our proposed method was tested with many hand gesture datasets and validated with different evaluation metrics. Experimental results show that our method detects more hand gestures as compared to the other existing methods with robust hand gesture recognition and corresponding emotion expressions, in real time.

• Proceedings of the KIEE Conference
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• 1994.07a
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• pp.273-276
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• 1994

It has been known the fact that extended Kalman filter (EKF) is correctly capable of estimating system parameters and state variables by eliminating virtually all influences of structural noises, and fuzzy controller is robust to parameter variations. this paper presents a design method of Reduced-Order EKF and fuzzy controller which consists of the rotor speed and the rotor flux estimation only by measuring stator currents. Experiment results show that both the rotor speed and the rotor flux can be prominently estimated in a wide range of the speed.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.2
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• pp.293-300
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• 2011

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 and adaptive fuzzy learning contrroller(AFLC) for speed control in IPMSM Drives. AFLC is chaged fuzzy rule base by rule base modifier for robust control of IPMSM. 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 and AFLC is confirmed by comparing to conventional algorithm.