• Journal of Power Electronics
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• v.11 no.4
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• pp.393-400
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• 2011

The main objective of this paper is to control the speed of Nonlinear Hybrid Electric Vehicle (HEV) by controlling the throttle position. Various control techniques such as well known Proportional-Integral-Derivative (PID) controller in conjunction with state feedback controller (SFC) such as Pole Placement Technique (PPT), Observer Based Controller (OBC) and Linear Quadratic Regulator (LQR) Controller are designed. Some Intelligent control techniques e.g. fuzzy logic PD, Fuzzy logic PI along with Adaptive Controller such as Self Organizing Controller (SOC) is also designed. The design objective in this research paper is to provide smooth throttle movement, zero steady-state speed error, and to maintain a Selected Vehicle (SV) speed. A comparative study is carried out in order to identify the superiority of optimal control technique so as to get improved fuel economy, reduced pollution, improved driving safety and reduced manufacturing costs.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.11a
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• pp.187-190
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• 2007

In this paper, we introduce concepts of entropy and similarity measure of interval-valued intuitionistic fuzzy sets (IVIFSs), discuss their relationship between similarity measure and entropy of IVIFSs, show that similarity measure and entropy of IVIFSs can be transformed by each other based on their axiomatic definitions and give some formulas to calculate entropy and similarity measure of IVIFSs.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.21 no.7
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• pp.715-725
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• 2010

Orthgonal Frequency Division Multiplexing(OFDM) system is effective for the high data rate transmission in the frequency selective fading channel. In this paper we propose PAPR(Peak to Average Power Ratio) reduction method of problem in OFDM system used Fuzzy theory that often control machine. This thesis proposes PAPR reducing method of OFDM system using Fuzzy theory. The advantages for using Fuzzy theory to reduce PAPR are that it is easy to manage the data and embody the hardware, and required smaller amount of operation. Firstly, we proposed simple algorithm that is reconstructed at receiver with transmitted overall PAPR which is reduced PAPR of sub-block using Fuzzy. Although there are some drawbacks that the operation of the system is increased comparing conventional OFDM system and it is needed to send the information about Fuzzy indivisually, it is assured that the performance of the system is enhanced for PAPR reducing. To evaluate the perfomance, the proposed search algorithm is compared with the proposed algorithm in terms of the complementary cumulative distribution function(CCDF) of the PAPR and the computational complexity. As a result of using the QPSK and 16QAM modulation, Fuzzy theory method is more an effective method of reducing 2.3 dB and 3.1 dB PAPR than exiting OFDM system when FFT size(N)=512, and oversampling=4 in the base PR of $10^{-5}$.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.399-403
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• 1998

In this paper, we proposed a new fuzzy supervised learning algorithm. We construct, and train, a new type fuzzy neural net to model the linear activation function. Properties of our fuzzy neural net include : (1) a proposed linear activation function ; and (2) a modified delta rule for learning algorithm. We applied this proposed learning algorithm to exclusive OR,3 bit parity using benchmark in neural network and pattern recognition problems, a kind of image recognition.

• Journal of the Korean Data and Information Science Society
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• v.17 no.2
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• pp.609-617
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• 2006

A new fuzzy c-regression algorithm for switching regression analysis is presented, which combines fuzzy c-means clustering and least squares support vector machine. This algorithm can detect outliers in switching regression models while yielding the simultaneous estimates of the associated parameters together with a fuzzy c-partitions of data. It can be employed for the model-free nonlinear regression which does not assume the underlying form of the regression function. We illustrate the new approach with some numerical examples that show how it can be used to fit switching regression models to almost all types of mixed data.

• Journal of information and communication convergence engineering
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• v.6 no.3
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• pp.337-342
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• 2008

Controlling spillway gates of dams is a complex, nonlinear, non-stationary control process and is significantly affected by hydrological conditions which are not predictable beforehand. In this paper, control methods based on dynamic fuzzy control are proposed for the operation of spillway gates of dams during floods. The proposed methods are not only suitable for controlling spillway gates but also able to maintain target water level in order to prepare a draught. In the proposed methods, we use dynamic fuzzy control that the membership functions can be varied by changing environment conditions for keeping up the target water level, instead of conventional static fuzzy control. Simulation results demonstrate that the proposed methods based on dynamic fuzzy control produce an accurate and efficient solution for both of controlling spillway gates and maintaining target water level defined beforehand.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1516-1520
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• 2005

We proposed the indirect adaptive fuzzy model based sliding mode controller to control a nonaffine nonlinear systems. Takagi-Sugano fuzzy system is used to represent the nonaffine nonlinear system and then inverted to design the controller at each sampling time. Also sliding mode component is employed to eliminate the effects of disturbances, while a fuzzy model component equipped with an adaptation mechanism reduces modeling uncertainties by approximating model uncertainties. The proposed controller and adaptive laws guarantee that the closed-loop system is stable in the sense of Lyapunov and the output tracks a desired trajectory asymptotically.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.2 no.1
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• pp.78-82
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• 2002

In this paper, we have studied a numerical stability analysis method for the robust fuzzy feedback linearization regulator using Takagi-Sugeno fuzzy model. To analyze the robust stability, we assume that uncertainty is included in the model structure with known bounds. For these structured uncertainty, the robust stability of the closed system is analyzed by applying Linear Matrix Inequalities theory following a transformation of the closed loop systems into Lur'e systems.

• Journal of Advanced Marine Engineering and Technology
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• v.36 no.7
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• pp.927-934
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• 2012

This paper suggests a new fuzzy PID controller with variable parameters which improves the shortage of the fuzzy PID controller with fixed parameters suggested in [9]. The derivation procedure follows the general design procedure of the fuzzy logic controller, while the resultant control law is the form of the conventional PID controller. Therefore, the suggested controller has two advantages. One is that it has only four fuzzy linguistic rules and analytical form of control laws so that the real-time control system can be implemented based on low-price microprocessors. The other is that the PID control action can always be achieved with time-varying PID controller gains only by adjusting the input and output scalers at each sampling time.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.1
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• pp.1-6
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• 2010

Ying[M.S. Ying, Linguistic quantifiers modeled by Sugeno integrals, Artificial Intelligence 170(2006) 581-606] studied a framework for modeling quantifiers in natural languages in which each linguistic quantifier is represented by a family of fuzzy measures and the truth value of a quantified proposition is evaluated by using Sugeno integral. In this paper, we consider interval-valued fuzzy measures and interval quantifiers which are the generalized concepts of fuzzy measures and quantifiers, respectively. We also investigate logical properties of a first order language with interval quantifiers modeled by the Sugeno integral with respect to an interval-valued fuzzy measures.