• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1445-1446
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• 2007

In this paper, we have studied for analysis of the partial discharge(PD) signal in power transmission line. The PD signal has estimated as detected signal accumulation of a PRPDA method by using Labview, and analyzed with neural-fuzzy algorithm. With practical PD logic implementation of theoretical detected system and hardware implementation, the device for Hipotronics Company's 22.9kV or 154kV setup have generated and then have applied with 18kV,20kV with 1:1 time probe. It's also used the LDPE 0.27mmt (scratch error 0.05mmt) to sample for making PD. Our new class of PD detected algorithm have also compared with previous PRPDA or Neural Fuzzy algorithm, which has diagnose more conveniently by adding numerical values.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.3
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• pp.329-336
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• 2009

In this paper, we propose the design of an optimized fuzzy cascade controller for rotary inverted pendulum system by means of Hierarchical Fair Competition-based Genetic Algorithms (HFCGA) which is a kind of parallel genetic algorithms. The rotary inverted pendulum system is the system for controlling the inclination of pendulum axis through the adjustment of rotating arm. The control objective of the system is to control the position of rotating arm and to make the pendulum maintain the unstable equilibrium point of vertical position. To control rotary inverted pendulum system, we designs the fuzzy cascade controller scheme consisted of two fuzzy controllers and optimizes the parameters of the designed controller by means of HFCGA. A comparative analysis between the simulation and the practical experiment demonstrates that the proposed HFCGA based fuzzy cascade controller leads to superb performance in comparison with the conventional LQR controller as well as HFCGA based PD cascade controller.

• International Journal of Control, Automation, and Systems
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• v.4 no.4
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• pp.438-447
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• 2006

Fuzzy controller's design depends mainly on the rule base and membership functions over the controller's input and output ranges. This paper presents two different approaches to deal with these design issues. A simple and efficient approach; namely, Fuzzy Subtractive Clustering is used to identify the rule base needed to realize Fuzzy PI and PD type controllers. This technique provides a mechanism to obtain the reduced rule set covering the whole input/output space as well as membership functions for each input variable. But it is found that some membership functions projected from different clusters have high degree of similarity. The number of membership functions of each input variable is then reduced using a similarity measure. In this paper, the fuzzy subtractive clustering approach is shown to reduce 49 rules to 8 rules and number of membership functions to 4 and 6 for input variables (error and change in error) maintaining almost the same level of performance. Simulation on a wide range of linear and nonlinear processes is carried out and results are compared with fuzzy PI and PD type controllers without clustering in terms of several performance measures such as peak overshoot, settling time, rise time, integral absolute error (IAE) and integral-of-time multiplied absolute error (ITAE) and in each case the proposed schemes shows an identical performance.

• Journal of the Korean Society of Industry Convergence
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• v.3 no.1
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• pp.3-8
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• 2000

This paper describes a development of self tuning scheme for PI and PO type fuzzy controllers. The output scaling factor(SF) is adjusted on-line by fuzzy rules according to the current trend of the controlled process. The rule-base for tuning the output SF is defined on error and change of error for the controlled variable using the most natural and unbiased membership functions. Simulation results demonstrate the better control performance can be achieved in comparison with Ziegler-Nichols(Z-N) PID controllers.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.141.1-141
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• 2001

This paper describes an approach for synthesizing a modularized gain scheduled PD type fuzzy logic controller(FLC) for a high maneuverable aircraft system, where the gains of FLC are on-line adapted according to the flight condition. Specially, the systematic procedure via root locus technique is carried out for the sellection of the gains of FLC. Simulation results demonstrate that the proposed gain scheduled fuzzy logic controller yields better control performance than the normal (without gain scheduling) fuzzy controller.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.193-196
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• 1996

A pneumatic cylinder has been used in the production facilities of various industries. However, it is difficult to achieve deciding the precise position of the piston rod, due to the nonlinear properties arising from the air compression and the friction. In recent years, the fuzzy control algorithm has been frequently applied to various kinds of systems on account of its simple algorithm, good adaptability to complex or nonlinear systems and so on. On the other hand, the PID or I-PD control has been used in many engineering fields because of the excellent performance. However, it is known that each one of them has disadvantages. In this paper, we propose a hybrid control which is strived to obtain the advantages of each other. It is shown that the proposed hybrid control performs better than the conventional I-PD control through the experimental results.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.12
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• pp.2313-2321
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• 2008

In order to develop reliable on-site partial discharge(PD) pattern recognition algorithm, the fuzzy neural network based on fuzzy set(FNN) and the polynomial network pattern classifier based on fuzzy Inference(PNC) were investigated and designed. Using PD data measured from laboratory defect models, these algorithms were learned and tested. Considering on-site situation where it is not easy to obtain voltage phases in PRPDA(Phase Resolved Partial Discharge Analysis), the measured PD data were artificially changed with shifted voltage phases for the test of the proposed algorithms. As input vectors of the algorithms, PRPD data themselves were adopted instead of using statistical parameters such as skewness and kurtotis, to improve uncertainty of statistical parameters, even though the number of input vectors were considerably increased. Also, results of the proposed neuro-fuzzy algorithms were compared with that of conventional BP-NN(Back Propagation Neural Networks) algorithm using the same data. The FNN and PNC algorithms proposed in this study were appeared to have better performance than BP-NN algorithm.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.11
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• pp.1-10
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• 1996

In this paper, we suggest a new identification method based on the takagi-sugeno fuzzy model which prepresents an envrionmental stiffness and propose a method to decide PD gains of the PD controller. It is difficult to perform a compliance task due to characteristics of robot itself and uncertain work envronment. Therefore, in this paper, we identify the fuzzy rule by dividing the relationship of input-output data into several piecewise-linear equations using the hough transform which is the one this fuzzy model, we propose a method to design the pD gain. We show the validity of this method by the experiment of tracking the surface of the paper box as an example of variable environment using robot manipulator and force sensing system. As a performance index, we use the settling time, and perform an analysis between conventional PD contorllers and this controller.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1410-1413
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• 1993

This paper presents the application of fuzzy control to three links of a Rhino robot and compares its performance to traditional PD control. The dynamics of motion of robot links are governed by nonlinear differential equations. The fuzzy controller, being an adaptive technique, gives better performance than the traditional linear PD controller over a typical operational range. The fuzzy controller reaches the desired position with no overshoot, which is unlikely with the PD controller.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.67.1-67
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• 2001

This paper presents a neural network based self-tuning fuzzy PI-PD control scheme for robust speed control of induction motor. The PID controller is being widely used in industrial applications. When continuously used long time, the electric and mechanical parameters of induction motor change, degrading the performance of PID controller considerably. This paper re-analyzes the fuzzy controller as conventional PID controller structure, and proposes a neural network based self-tuning fuzzy PI-PD controller whose scaling factors are adjusted automatically. Proposed scheme is simple in structure and computational burden is small ...