• Journal of Institute of Control, Robotics and Systems
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• v.12 no.7
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• pp.644-649
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• 2006

In this paper, a methodology for estimating the model parameters of a discrete-time system and tuning a digital PID controller based on the estimated model and a genetic algorithm is presented. To deal with optimization problems regarding parameter estimation and controller tuning, pseudo-parallel genetic algorithms(PPGAs) are used. The parameters of a discrete-time system are estimated using both the model adjustment technique and a PPGA. The digital PID controller is described by the pulse transfer function and then its three gains are tuned based on both the model reference technique and another PPGA. A set of experimental works on two processes are carried out to illustrate the performance of the proposed method.

• The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.9 no.3
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• pp.97-103
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• 1995

In this paper, for fuzzy control of linear servo system using the moving coil type linear DC motor, we propose a new fuzzy control method using parameter tuning for membership functions. A proposed fuzzy control method tunes parameters of membership function to have an appropriate control input signal for system when error exceeds predefined value and makes an inference using conventional fuzzy control rules when error reduces to a predefined value. To verify usefulness of a proposed fuzzy control method, making simulation and experiment, we compare with characteristics for conventional fuzzy control method.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2005.06a
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• pp.314-320
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• 2005

In this paper, a methodology for estimating the parameters of a discrete-time system and designing a digital PID controller based on the estimated model and a genetic algorithm is presented. To deal with optimization problems occurring regarding parameter estimation and controller design, a pseudo parallel genetic algorithm (PPGA) is used. The parameters of a discrete-time system are estimated using both the model technique and a PPGA. The digital PID controller is described by the pulse transfer function and its parameters are tuned based on both the model reference technique and another PPGA. A set of experimental works on two processes are carried out to illustrate the performance of the proposed method.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.24 no.9
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• pp.118-123
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• 2010

The selection of flux level in the maximum torque control of stator flux-oriented induction motor drives in the field weakening region is dependent on stator resistance and inductances. This paper presents parameter detuning effects of stator flux-oriented control drives in the field weakening region. The detuning effects of stator resistance and rotor leakage inductance are analyzed. The decrease of torque and the flux control lost by the detuning of inductance are shown in the simulation results.

• Proceedings of the KIPE Conference
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• 2007.07a
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• pp.394-396
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• 2007

In this paper proposed to neural network PI self-tuning direct controller using Error back propagation algorithm. Proposed controller applies to speed controller and current controller. Also, this built up the interface environment to drive it simply and exactly in any kind of reference, environment fluent and parameter transaction of PMSM. Neural network PI self-tuning simulator using Visual C++ and Matlab Simulation is organized to construct this environment, Built up interface has it's own purpose that even the user who don't have the accurate knowledge of Neural network can embody operation characteristic rapidly and easily.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 1998.05a
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• pp.321-326
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• 1998

본 논문에서는 정밀 제어와 온-라인 제어를 위하여 유전 알고리즘을 이용한 퍼지-신경망 제어기를 제안하였다. 제안된 제어기의 설계방법은 다음과 같은 3단계의 동조과정으로 구성한다. 1) 퍼지 제어기의 비퍼지화 연산을 신경망을 이용하여 함수근사화 시킨 후, 퍼지-신경망 제어기를 구성한다. 2) 플랜트에 적합한 퍼지 소속함수의 형태를 얻기 위해 유전 알고리즘을 이용하여 근사화된 퍼지 소속함수를 찾는다. 3) 근사화된 초기 퍼지 소속함수를 퍼지-신경망 제어기에 의해 적응학습으로 최적의 퍼지 소속함수를 얻고, 또한 플랜트의 파라미터 변동이나 외부환경의 변화에 대해 적응할 수 있도록 최적의 퍼지 소속함수를 추정한다. 제안된 제어기의 성능을 평가하기 위하여 DC 서보모터의 속도제어에 적용하였다.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.23 no.10
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• pp.59-68
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• 2009

The field oriented control of induction motors is widely used in high performance applications. However, detuning caused by parameter disturbance still limits the performance of these drives. In order to accomplish variable speed operation, conventional PI-like controllers are commonly used. These controllers provide limited good performance over a wide range of operation, even under ideal field oriented conditions. This paper is proposed high performance control of induction motor drive using multi adaptive fuzzy controller. This controller has been performed for speed control with fuzzy adaptation mechanism (FAM)-PI, current control with model reference adaptive fuzzy control(MFC) and estimation of speed using ANN. The proposed control algorithm is applied to induction motor drive system using FAM-PI, MFC and ANN controller. The performance of proposed controller is evaluated by analysis for various operating conditions using parameters of induction motor drive. Also, this paper is proposed the analysis results to verify the effectiveness of this controller.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.1
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• pp.81-86
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• 2005

In this study, we introduce a new category of fuzzy inference systems based on information granulation to carry out the model identification of complex and nonlinear systems. Informally speaking, information granules are viewed as linked collections of objects (data, in particular) drawn together by the criteria of proximity, similarity, or functionality Granulation of information with the aid of Hard C-Means (HCM) clustering help determine the initial parameters of fuzzy model such as the initial apexes of the membership functions and the initial values of polynomial functions being used in the premise and consequence part of the fuzzy rules. And the initial parameters are tuned effectively with the aid of the genetic algorithms(GAs) and the least square method (LSM). An aggregate objective function with a weighting factor is also used in order to achieve a balance between performance of the fuzzy model. The proposed model is evaluated with using a numerical example and is contrasted with the performance of conventional fuzzy models in the literature.

• Journal of the Korean Institute of Intelligent Systems
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• v.5 no.4
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• pp.101-110
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• 1995

In this paper, a DC servo motor position control system based on acceleration control is proposed. The proposed control system consists of an acceleration controller and an auto-tuqing fuzzy PID controller. The auto-tuning fuzzy PID controller provides corrections for an acceleration reference to remove the effect of parametric uncertainties. And it comprises of the expert system which performs the automatic tuning of the PID controller parameters and the conventional PID controller. Expermental results demonstrate strate thi~tth e proposed overall control system has robust properties and good control performances with regard to unmeasurable disturbances and parameter variations. Therefore, the proposed control scheme enhances the applicability of an acceleration control approach and especially performs accurate position control under such an operating environment that model uncertainties exist and/or load, etc. change significantly.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.15 no.4
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• pp.14-22
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• 2001

In this paper, we design the Adaptive Neuro-Fuzzy Precompensator(ANFP) for the suppression of low-frequency oscillation and the improvement of system stability. Here, ANFP is designed to compensate the conventional Power System Stabilizer(PSS). This design technique has the structural merit that is easily implemented by adding ANFP to an existing PSS. Firstly, the Fuzzy Precompensator with Loaming ability is constructed and is directly learned from the input and output data of the generating unit. Because the ANFP has the property of learning, fuzzy rules and membership functions of the compensator can be automatically tuned by teaming algorithm Loaming is based on the minimization of the ems evaluated by comparing the output of the ANFP and a desired controller. Case studies show the 7posed schema can be provided the good damping of the power system over the wide range of operating conditions and improved dynamic performance of the system.