• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 C
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• pp.1242-1244
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• 1999

AVR parameter tuning for voltage control of power system generators has generally been done with the open-circuit model of the synchronous generator. When the generator is connected on-line and operating at rated load conditions, the AVR operates in an entirely different environment from the open-circuit conditions. This paper describes a new method for AVR parameter tuning using optimization technique with on-line linearized system model. As this method considers not only the on-line models but also the off-line open-circuit models, AVR parameters tuned by this method can give the sufficiently stable performance at the open-circuit commissioning phase and give the desired performance at the operating conditions. Also this method estimates the optimum parameters for desired performance indices that are chosen for satisfying requirements in some practical applications, the performance of the AVR can satisfy the various requirements.

• 한국지능시스템학회논문지
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• 제6권3호
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• pp.40-50
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• 1996

By using the neural networks(NN) as system identifier, the on-line self tuning method for fuzzy controller(FC) is proposed. In theis method, the learning of NN is carried out during control operation of FC and the cinsequent parameters of FC is tuned on-line automatically by means of system output errors backpropagated through NN. The Sugeno fuzzy model with constants as consequent parameters is selected for simplifying computation. In procedures of parameter tuning, the gradient descent method is used and the gradient vectors for adjusting the weight of NN are transferred as controller output errors. To evaluate the performance, the proposed method is applied to the inverted pendulum system.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1991년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 22-24 Oct. 1991
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• pp.238-242
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• 1991

This paper presents an approach to self-tuning PI control of dynamic plants, based on fuzzy logic application. A fuzzy logic composed of linguistic conditional statements is employed by defining the relations of input-output variables of the controller. In the synthesis of a fuzzy logic controller, one of the most difficult problem is the selection of linguistic control rules and parameters. To overcome this difficulty, self-tuning fuzzy PI controller (STFPIC) with a hierarchical structure in which the fuzzy PI controller is assigned as the lower level and the rule modification and parameter adjustment as the higher level. The rules and parameters are generated by the adjustment of membership function through performance index(PE). In this paper, the algorithm for of the controller performance is estimated by means of computer simulation.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1989년도 한국자동제어학술회의논문집; Seoul, Korea; 27-28 Oct. 1989
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• pp.786-789
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• 1989

A nonlinear self-tuning regulator for a neutralization process of a weak acid and strong base system is proposed. Rearranging the state equation of the process model, we first obtain equations which are linear for a manipulated variable or unknown parameters. Then to these equations we apply the standard procedure used in designing linear self-tuning regulators. Simulation results show that the regulator provides very good performances for various realistic situations and traces variations of the unknown parameters. Since computations are simple and additional measurements except the effluent pH value are only flow rates of influent streams, it can be easily applied to real processes such as a waste water treatment process.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2008년도 제39회 하계학술대회
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• pp.13-14
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• 2008

This paper describes the optimal PSS parameter design for the PSS of EX2000 excitation system. The suggested tuning technique uses the model-based PSS tuning method which have three steps: generation system modeling, determination of PSS parameters, and on-site test. Using this method, the PSS parameters of EX2000 system in Dangjin T/P #4 was designed and verified by linear analysis program, PSS/E, and EMTDC/PSCAD.

• 한국마린엔지니어링학회:학술대회논문집
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• 한국마린엔지니어링학회 2005년도 전기학술대회논문집
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• pp.268-273
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• 2005

The conventional PID controller has been widely used in many industrial control system because engineers can easily understand how to deal with three parameters of PID controller. The conventional tuning methods, however, have a tendency depend on experience and experiment. In this paper a real-coded genetic algorithm is used to search for the optimal parameters of PID controller for marine diesel engine. Simulation results compared with conventional PID controller tuning methods show the effectiveness and good performance of proposed scheme.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1997년도 하계학술대회 논문집 B
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• pp.379-382
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• 1997

We present a hybrid self-tuning method using hyper elliptic Gaussian membership function. The proposed method applies a GA to identify the structure and the parameters of a fuzzy inference system. The parameters obtained by a GA, however, are near optimal solutions. So we solve this problem through a backpropagation-type gradient method. It is called GA hybrid self-tuning method in this paper. We provide a numerical example to evaluate the advantage and effectiveness of the proposed approach and compare with the conventional method.

• 제어로봇시스템학회논문지
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• 제6권9호
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• pp.739-744
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• 2000

A robust control algorithm under disturbance and reference change is developed using a self tuning control method incorporting of the well known internal model principle and the annihilator polynomical. The types of disturbance and reference signal are assumed to be given as known difference polynomials. The algorithm is shown for a minimum phase system with parameters of unknown parameters.

• 대한전기학회논문지:전기기기및에너지변환시스템부문B
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• 제51권12호
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• pp.691-696
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• 2002

This paper presents a neural network based self-tuning fuzzy PID control scheme with variable learning rate for induction motor speed control. When induction motor is continuously used long time, its electrical and mechanical Parameters will change, which degrade the Performance of PID controller considerably. This Paper re-analyzes the fuzzy controller as conventional PID controller structure, introduces a single neuron with a back-propagation learning algorithm to tune the control parameters, and proposes a variable learning rate to improve the control performance. Proposed scheme is simple in structure and computational burden is small. The simulation using Matlab/Simulink and the experiment using dSPACE(DS1102) board are performed to verify the effectiveness of the proposed scheme.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2024년도 춘계학술발표대회
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• pp.794-797
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• 2024

Despite advancements in deep learning, existing semantic segmentation models exhibit suboptimal performance under adverse weather conditions, such as fog or rain, whereas they perform well in clear weather conditions. To address this issue, much of the research has focused on making image or feature-level representations weather-independent. However, disentangling the style and content of images remains a challenge. In this work, we propose a novel fine-tuning method, 'freeze-n-update.' We identify a subset of model parameters that are weather-independent and demonstrate that by freezing these parameters and fine-tuning others, segmentation performance can be significantly improved. Experiments on a test dataset confirm both the effectiveness and practicality of our approach.