• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1092-1095
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• 1996

In this paper The tuning of PID controller for multi input-output is studied by using fuzzy inference. State of coupling is estimated by fuzzy inference, its results is used for tuning of PID controller to get optimum P,I,D parameter with regard to state of coupling. This method is simulated to Turbo-generating system with $2{\times}2$ multi input-output and made with electronic circuit, its response is very satisfactory.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.44 no.6
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• pp.21-28
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• 2007

Using multi-resolution, the mutiresolution proportional-integral-derivative(MRPID) controller functions as a filter to eliminate noise and disturbance which are included in error signals. If the sampling frequency is high, the response time will be delayed because of the remaining high frequency component although the overshoot is removed. However, if the sampling frequency is low, the response time will be enhanced by getting rid of signal components while the overshoot is increased. In this paper, the sampling frequency tuning method is used the response of the proportional integral derivative(PID) controller and the MRPID controller, and the parameter tuning method is considered the characteristic of the MRPID controller. The proposal method is verified by computer simulations.

• 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 KIEE Conference
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• 1993.07a
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• pp.316-318
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• 1993

In order to estimation the optimum PID parameter of the turbine speed controller, the response cure of the object plant was compared with the reference pattern and then the magnitude peak value error and peak time error was calculated. With the calculated errors as input into the Fuzzy inference Method was introduced to propose the tuning method for each parameter. And the computer simulation was performed with the above Fuzzy inference method in which the Chunju hydro power plant turbine governor system was used as a model. This Study also aims to develop the exclusive tuner for govenor using industrial computer.

• Proceedings of the KIEE Conference
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• 1993.07a
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• pp.214-217
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• 1993

This paper presents a new identification method which utilizes fuzzy inference in parameter identification. The proposed system has an additional control loop where a real plant is replaced by a plant model. The control system to be designed is to satisfy the following specifications: 1) It has zero steady-state error. 2) It has adequate damping characteristics. 3) 1),2) satisfied, it has a shortest rise-time. Fuzzy rules describe the relationship between comparison results of the features and magnitude of modification in the model parameter values. This method is effective in auto-tuning because the response of the closed loop is verified. The proposed method is tested in simulation for several plants with first- order lags and dead-times. The results show that the proposed method is effective in practical use.

• The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.9 no.1
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• pp.63-66
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• 1995

PID controller is being used in many servo control system. However, when a control system has variable load, it is difficult to guarantee the accurate control of the system. In the way of solving this problem, in this paper, a auto-tuning method of PID controller parameter using fuzzy rule in variable load is presented. The parameter of PID controller are decided by fuzzy rule according to load variation. The accurate control function of fuzzy auto-tuning is demonstrated by simulation.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.11
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• pp.22-33
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• 1999

This paper presents the method for the automatic tuning of a design weighting polynomial parameters of a generalized minimum-variance stochastic ultivariable self-tuning controller which adapts to changes in the higher order nonminimum phase system parameters with time delays and noises. The self-tuning effect is achieved through the recursive least square algorithm at the parameter estimation stage and also through the Robbins-Monro algorithm at the stage of optimizing the design weighting polynomial parameters of the controller. The proposed multivariable self-tuning method is simple and effective compared with pole restriction method. The computer simulation results are presented to adapt the higher order multivariable system with nonminimum phase and with changeable system parameters.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.49 no.12
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• pp.589-594
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• 2000

The fuzzy controllers have been applied to the power system stabilizer due to its excellent properties on the nonlinear systems. But the design process of fuzzy controller requires empirical and heuristic knowledge of human experts as well as many trial-and-errors in general. This process is time consuming task. This paper presents an parameters tuning method of the fuzzy power system stabilizer using the genetic algorithm and simulated annealing(SA). The proposed method searches the local minimum point using the simulated annealing algorithm. The proposed method is applied to the one-machine infinite-bus of a power system. Through the comparative simulation with conventional stabilizer and fuzzy stabilizer tuned by genetic algorithm under various operating conditions and system parameters, the robustness of fuzzy stabilizer tuned by proposed method with respect to the nonlinear power system is verified.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.206-211
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• 1993

This paper presents a new identification method which utilizes fuzzy inference in parameter identification. The proposed system has an additional control loop where a real plant is replaced by a plant model. The control system to be designed is to satisfy the following specifications: 1) It has zero steady-state error. 2) It has adequate damping characteristics. 3) 1),2) satisfied, it has a shortest rise-time. Fuzzy rules describe the relationship between comparison results of the features and magnitude of modification in the model parameter values. This method is effective in auto-tuning because the response of the closed loop is verified. The proposed method is tested in simulation for several plants with high-order lags and dead-times.

• Communications for Statistical Applications and Methods
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• v.24 no.6
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• pp.673-683
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• 2017

The least absolute shrinkage and selection operator (LASSO) has been a popular regression estimator with simultaneous variable selection. However, LASSO does not have the oracle property and its robust version is needed in the case of heavy-tailed errors or serious outliers. We propose a robust penalized regression estimator which provide a simultaneous variable selection and estimator. It is based on the rank regression and the non-convex penalty function, the smoothly clipped absolute deviation (SCAD) function which has the oracle property. The proposed method combines the robustness of the rank regression and the oracle property of the SCAD penalty. We develop an efficient algorithm to compute the proposed estimator that includes a SCAD estimate based on the local linear approximation and the tuning parameter of the penalty function. Our estimate can be obtained by the least absolute deviation method. We used an optimal tuning parameter based on the Bayesian information criterion and the cross validation method. Numerical simulation shows that the proposed estimator is robust and effective to analyze contaminated data.