• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.506-508
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• 1998

In this paper, the optimal digital redesign is studied within the framework of fuzzy systems and dual-rate sampling control theory. An equivalent fast-rate discrete-time state-space model of the continuous-time system is constructed by using fuzzy inference systems. To obtain the optimal feedback gains developed in the continuous-time system, the constructed fuzzy system is converted into a continuous-time system. The developed continuous-time control law is converted into an equivalent slow-rate digital control law using the proposed digital redesign method. The digital redesign technique using a fuzzy model is employed to simulate the inverted pendulum dynamics.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.9 no.2
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• pp.141-146
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• 2009

Support vector machine is a classification algorithm based on statistical learning theory. It has shown many results with good performances in the data mining fields. But there are some problems in the algorithm. One of the problems is its heavy computing cost. So we have been difficult to use the support vector machine in the dynamic and online systems. To overcome this problem we propose to use stratified sampling of statistical sampling theory. The usage of stratified sampling supports to reduce the size of training data. In our paper, though the size of data is small, the performance accuracy is maintained. We verify our improved performance by experimental results using data sets from UCI machine learning repository.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.11
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• pp.997-1004
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• 2012

In enterprise software projects, performance issues have become more critical during recent decades. While developing software products, many performance tests are executed in the earlier development phase against the newly added code pieces to detect possible performance regressions. In our previous research, we introduced the framework to enable automated performance anomaly detection and reduce the analysis overhead for identifying the root causes, and showed Statistical Process Control (SPC) can be successfully applied to anomaly detection. In this paper, we explain the special performance trend in which the existing anomaly detection system can hardly detect the noticeable performance change especially when a performance regression is introduced and recovered again a while later. Within the fixed number of sampling period, the fluctuation gets aggravated and the lower and upper control limit get relaxed so that sometimes the existing system hardly detect the noticeable performance change. To resolve the issue, we apply dynamically tuned sampling window size based on the performance trend, and Fuzzy theory to find an appropriate size of the moving window.

• Journal of Biosystems Engineering
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• v.25 no.2
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• pp.107-114
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• 2000

Low temperature storage method is to increase the value of agricultural products by reducing quality loss and regulate consignment time by controlling respiration rates of agricultural products. Respiration rate of agricultural products depends on several factors such as temperature, moisture, gas composition and a microbe inside the storage room. Temperature is the most important factor among these, which affects respiration rate and causes low or high temperature damage. Fuzzy logic was used to control the temperature of a storage room ,which uses information of uncertain facts and mathematical model for room temperature control . Room temperature was controlled better by using fuzzy logic control method rather than on-off control method. Refrigerant flow rates and temperature deviations were measured for on-off system using TEV(temperature expansion valve) and for fuzzy system using EEV(Electrical Expansion Valve) . Temperature of the Storage room was lowered faster by using fuzzy system than on -off system. Temperature deviation was -0.6~+0.9$^{\circ}C$ for on-off system and $\pm$0.2$^{\circ}C$ for fuzzy system developed. Temperature deviation and variation of temperature deviation were used as inout parameters for fuzzy system. The most suitable input and output value were found by experiment. Cooling rate of the storage room decreased while temperature deviation increased for the sampling time of 20 sec.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.2
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• pp.151-156
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• 2008

This paper presents the stability analysis and design for a sampled-data fuzzy control system with neutral type of time delay, which is formed by a nonlinear plant and a sampled-data fuzzy controller connected in a closed loop. The sampling activity and neutral type of time delay will complicate the system dynamics and make the stability analysis much more difficult than that for a pure continuous-time fuzzy control system. Based on the fuzzy-model-based control approach, LMI(linear matrix inequality)-based stability conditions are derived to guarantee the nonlinear networked system stability. An application example will be given to show the merits and design a procedure of the proposed approach.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.220-222
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• 2006

This paper presents intelligent digital redesign method of global approach for hybrid state space fuzzy-model-based controllers. For effectiveness and stabilization of continuous-time uncertain nonlinear systems under discrete-time controller, Takagi-Sugeno(TS) fuzzy model is used to represent the complex system. And global approach design problems viewed as a convex optimization problem that we minimize the error of the norm bounds between nonlinearly interpolated lineal operators to be matched. Also, by using the bilinear and inverse bilinear approximation method, we analyzed nonlinear system's uncertain parts more precisely. When a sampling period is sufficiently small, the conversion of a continuous-time structured uncertain nonlinear system to an equivalent discrete-time system have proper reason. Sufficiently conditions for the global state-matching of the digitally controlled system are formulated in terms of linear matrix inequalities (LMIs). Finally, a T-S fuzzy model for the chaotic Lorentz system is used as an example to guarantee the stability and effectiveness of the proposed method.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.449-454
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• 1994

This paper describes new PID control methods based on the fuzzy logic. PID gains are retuned after evaluating control performances of transient responses in terms of performance features. The retuning procedure is based on fuzzy rules and reasoning accumulated from the knowledge of experts on PID gain scheduling. For the case that the retuned PID gains result in worse CLDR (characteristics of load disturbance rejection) than the initial gains, an on-line tuning scheme of the set-point weighting parameter is, proposed. This is based on the fact that the set-point weighting method efficiently reduce either overshoot or undershoot without any degradation of CLDR. The set-point weighting parameter is adjusted at each sampling instant by the fuzzy rules and reasoning. As a result, better control performances were achived in comparison with die controllers tuned by the Z-N (Ziegler-Nichols) parameter tuning formula or by the fixed set-point weighting parameter.

• International Journal of Control, Automation, and Systems
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• v.4 no.4
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• pp.506-515
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• 2006

This paper studies an intelligent digital control for nonlinear systems with multirate sampling. It is worth noting that the multirate control design is addressed for a given nonlinear system represented by Takagi-Sugeno (T-S) fuzzy models. The main features of the proposed method are that i) it is provided that the sufficient conditions for stabilization of the discrete-time T-S fuzzy system in the sense of Lyapunov stability criterion, which is can be formulated in the linear matrix inequalities (LMIs); and ii) the stability properties of the trivial solution of the digital control system can be deduced from that of the solution of its discretized versions. An example is provided for showing the feasibility of the proposed method.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.1783-1784
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• 2008

This paper presents a novel control technique to deal with networked control systems with neutral timedelay, which is known to highly degrade the control performance of the controlled system. The stability analysis and design method for a sampled-data fuzzy controller for discrete networked control systems (NCS). The neutral time-delay and sampling activity will complicate the NCS. And it make the stability analysis much more difficult than that for a continuous-time NCS. Based on the fuzzy control approach, linear matrix inequality (LMI)-based stability conditions are derived to guarantee the neutral T-S fuzzy system stability. The simulation results and practical experiments illustrate that the proposed controller design is realistic.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2286-2288
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• 2003

In this paper, a novel and efficient discretization method of a continuous-time Takagi-Sugeno (TS) fuzzy system is proposed. Because of the highly complex and nonlinear interaction among the subsystems, it is very difficult to develop the discretized version of continuous-tine TS fuzzy system. More precisely, to obtain the suitable discretized version, it is necessary to solve two main problems: to discretize global state equations of the continuous-tine TS fuzzy system and to hold the polytopic structure after the discretization. This paper will show the solution to above two problems. Main key idea is to transforme the sampling period into the intersampling period. Finally, To show the feasibility and the validity of the proposed method, a computer simulation is provided.