• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.29.1-29
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• 2001

Neuro-fuzzy systems are multi-layered connectionist networks that realize the elements and functions of traditional fuzzy logic control/decision systems. A trained neuro-fuzzy system is isomorphic to a fuzzy logic system, and fuzzy IF-THEN rule knowledge can be explicitly extracted from the network. This talk presents a brief introduction to self-adaptive neuro-fuzzy systems and addresses some recent research results and applications. Most of the existing neuro-fuzzy systems exhibit several major drawbacks that lead to performance degradation. These drawbacks are the curse of dimensionality (i.e., fuzzy rule explosion), inability to re-structure their internal nodes in a changing environment, and their lack of ability to extract knowledge from a given set of training data. This talk focuses on our investigation of network architectures, self-adaptation algorithms, and efficient learning algorithms that will enable existing neuro-fuzzy systems to self-adapt themselves in an unstructured and uncertain environment.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.14 no.4
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• pp.278-284
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• 2002

The work presented here provides a control of the supply duct outlet air temperature in PEM (personal environment module) using fuzzy PID controller. In previous work, PID control systems were used, but the result shows that the outlet air temperature and electric heater regulating voltage were oscillated. Fuzzy PID control systems are designed to improve the system response obtained using PID control and implemented experimentally Also, PID controller and fuzzy controller without PID logic are provided to compare the result with that of the fuzzy PID controller. Data obtained shows that the fuzzy PID control system satisfies the design criteria and works proper1y in controlling the supply air temperature. Also it has bettor performance than the previous result obtained using PID control.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.10a
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• pp.443-446
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• 2004

This paper discusses an intelligent digital redesign technique for designing a fuzzy pulse-width-modulated (PWM) control. First when we are given a well-designed fuzzy analog control, the equivalent digital control is intelligently redesigned. Using the similar technique we intelligently redesign the fuzzy PWM control from the intelligently redesigned fuzzy digital control. A stabilizability of the intelligently redesigned PWM control is rigorously analyzed.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.1
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• pp.113-118
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• 2006

This paper presents a new linear-matrix-inequality-based intelligent digital redesign (LMI-based IDR) technique to match the states of the analog and the digital T-S fuzzy control systems at the intersampling instants as well as the sampling ones. The main features of the proposed technique are: 1) the affine control scheme is employed to increase the degree of freedom; 2) the fuzzy-model-based periodic control is employed, and the control input is changed n times during one sampling period; 3) The proposed IDR technique is based on the approximately discretized version of the T-S fuzzy system, but its discretization error vanishes as n approaches the infinity. 4) some sufficient conditions involved in the state matching and the stability of the closed-loop discrete-time system can be formulated in the LMIs format.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.877-880
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• 1993

Fuzzy control algorithms are developed based on fuzzy models of systems. The control issues are posed as multiobjective optimization problems involving goals and constraints imposed on system's variables. Two basic design modes embrace on-and off-line control development. The first type of design deals with the time and state-dependent objectives and pertains to control determination based upon the current state of the system. The second design mode gives rise to explicit forms of fuzzy controller that is learned based on a given list of state-control associations. Both the fuzzy models as well as fuzzy controllers are realized as logic processors.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.2
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• pp.155-160
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• 2006

In the paper a robust indirect adaptive fuzzy controller is proposed for balancing and position control of the inverted pendulum system. Because balancing control rules of the pendulum and position control rules of the cart can be opposite, it is difficult to design an adaptive fuzzy controller that satisfy both objectives. To stabilize the pendulum at a specified position, the proposed fuzzy controller consists of a robust indirect adaptive fuzzy controller for balancing and a supervisory fuzzy controller which emulates heuristic control strategy and arbitrate two control objectives. It is proved that the signals in the overall system are bounded. Simulation results are given to verify the proposed adaptive fuzzy control method.

• International Journal of Control, Automation, and Systems
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• v.2 no.2
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• pp.182-188
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• 2004

This paper presents a design method of delay-dependent control for T-S fuzzy systems with time delays. Based on parallel distributed compensation (PDC) and a descriptor model transformation of the system, a delay-dependent control is utilized. An appropriate Lyapunov-Krasovskii functional is chosen for delay-dependent stability analysis. A sufficient condition for delay-dependent control is represented in terms of linear matrix inequalities (LMIs).

• Journal of Advanced Marine Engineering and Technology
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• v.23 no.4
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• pp.583-592
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• 1999

In order to control systems which contain nonlinearities control strategies must deal with the effects of them. Since most of control methods based on system mathematical models have been mainly developed focused on stability robustness against nonlinearities or uncertainties under the assumption that controlled systems are linear time invariant they have certain amount of limita-tions to smartly improve control perfomances of systems disturbed by nonlinearities or uncertainties. In this paper the fuzzy PID control law is suggested which can improve control performances of the nonlinear heavy load hydraulic systems disturbed by nonlinearities and uncertainties. Although the derivation process is based on the design process similar to general fuzzy logic con-troller resultant control law has analytical forms with time varying PID gains rather than linguis-tic forms so that implementation using commn-used versatile microprocessors can be achieved easily and effectively in real-time control aspect.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.1
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• pp.18-21
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• 2014

We propose a nonlinear observer design method for traffic control systems based on T-S fuzzy approach. We parameterize the observer gains in terms of the solution matrices of LMIs. We also give a simple algorithm to compute the observer gain matrices. Finally we give simulation results to show the effectiveness of the proposed fuzzy observer design method.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.268-271
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• 2004

This paper describes the development of a fuzzy logic control based on PID controller to improve the performances of the control system using conventional PID controller for the cascade process control systems. The structure of the proposed control system consists of two fuzzy-based PID controllers. One is used to eliminate the input disturbances of the inner loop and the other is used to regulate output response of the outer loop. The fuzzy PID design is derived from the linear-time continuous function of the conventional PID controller. The performance of the proposed controller is verified by MATLAB/SIMULINK simulation. Results of simulation studies demonstrates the outstanding of the control system using fuzzy-based PID controller in terms of reduced overshoot and fast response compared with the conventional PID controller.