• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.11
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• pp.753-759
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• 2004

This paper presents a modeling and simulation of a fuzzy controller for maximum power extraction of a grid-connected wind energy conversion system with a link of a rectifier and an inverter. It discusses the maximum power control algorithm for a wind turbine and proposes, in a graphical form, the relationships of wind turbine output, rotor speed, power coefficient, tip-speed ratio with wind speed when the wind turbine is operated under the maximum power control. The control objective is to always extract maximum power from wind and transfer the power to the utility by controlling both the pitch angle of the wind turbine blades and the inverter firing angle. Pitch control method is mechanically complicated, but the control performance is better than that of the stall regulation method. The simulation results performed on MATLAB will show the variation of generator's rotor angle and rotor speed, pitch angle, and generator output.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.1183-1187
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• 1993

The objective of this study is to design a fuzzy logic controller(FLC) which controls the position of excavator's attachment a noble FLC is proposed, which is based on simple control rules while offering easy tuning of control parameters by utilizing real operation characteristics of an operator. The proposed FLC consists of two parts, the proportional controller part and the FLC part. Experiments are carried out on a test bed which is built around a commercial excavator. The controller is applied to bhe leveling of excavator's bucket tip, which is one of the main functions in an excavator's operation.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1997.10a
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• pp.70-75
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• 1997

This paper proposes a new approach to the design of fuzzy-neuro control for track vehicle system using fuzzy logic based on neural network. The proposed control scheme uses a Gaussian function as a unit function in the neural network-fuzzy, and back propagation algorithm to train the fuzzy-neural network controller in the framework of the specialized learning architecture. It is proposed a learning controller consisting of two neural network-fuzzy based of independent reasoning and a connection net with fixed weights to simply the neural networks-fuzzy. The performance of the proposed controller is illustrated by simulation for trajectory tracking of track vehicle speed.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.2
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• pp.187-193
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• 2003

In this paper, a systematic design method of the intelligent PAM fuzzy controller for nonlinear systems using the efficient tools-Linear Matrix Inequality and the intelligent digital redesign is proposed. In order to digitally control the nonlinear systems, the TS fuzzy model is used for fuzzy modeling of the given nonlinear system. The convex representation technique also can be utilized for obtaining TS fuzzy models. First, the analog fuzzy-model-based controller is designed such that the closed-loop system is globally asymptotically stable in the sense of Lyapunov stability criterion. The simulation results strongly convince us that the proposed method has great potential in the application to the industry.

• The Transactions of the Korea Information Processing Society
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• v.13 no.2
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• pp.13-17
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• 2024

In this paper, union-based rule antecedent neuro-fuzzy controller, which can guarantee a parsimonious knowledge base with reduced number of rules, is proposed. The proposed neuro-fuzzy controller allows union operation of input fuzzy sets in the antecedents to cover bigger input domain compared with the complete structure rule which consists of AND combination of all input variables in its premise. To construct the proposed neuro-fuzzy controller, we consider the multiple-term unified logic processor (MULP) which consists of OR and AND fuzzy neurons. The fuzzy neurons exhibit learning abilities as they come with a collection of adjustable connection weights. In the development stage, the genetic algorithm (GA) constructs a Boolean skeleton of the proposed neuro-fuzzy controller, while the stochastic reinforcement learning refines the binary connections of the GA-optimized controller for further improvement of the performance index. An inverted pendulum system is considered to verify the effectiveness of the proposed method by simulation and experiment.

• International Journal of Automotive Technology
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• v.8 no.3
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• pp.383-394
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• 2007

Due to the clutch's non-linear dynamics, time-delays, external disturbance and parameter uncertainty, the automated clutch is difficult to control precisely during the launch process or automatic mechanical transmission (AMT) vehicles. In this paper, an enhanced fuzzy sliding mode controller (EFSMC) is proposed to control the automated clutch. The sliding and global stability conditions are formulated and analyzed in terms of the Lyapunov full quadratic form. The chattering phenomenon is handled by using a saturation function to replace the pure sign function and fuzzy logic adaptation system in the control law. To meet the real-time requirement of the automated clutch, the region-wise linear technology s adopted to reduce the fuzzy rules of the EFSMC. The simulation results have shown hat the proposed controller can achieve a higher performance with minimum reaching time and smooth control actions. In addition, our data also show that the controller is effective and robust to the parametric variation and external disturbance.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.51 no.2
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• pp.60-67
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• 2002

This paper presents a control approach for designing a self tuning fuzzy controller for a synchronous generator excitation and SVC system. A combination of thyristor-controlled reactors and fixed capacitors (TCR-FC) type SVC is recognized as having the most flexible control and high speed response, which has been widely utilized in power systems, is considered and designed to improve the response of a synchronous generator, as well as controlling the system voltage. The proposed parameter self tuning algorithm of fuzzy controller is based on the steepest decent method using two direction vectors which make error between inference values of fuzzy controller and output values of the specially selected PSS reduce steepestly. Using input-output data pair obtained from PSS, the parameters in antecedent part and in consequent part of fuzzy inference rules are learned and tuned automatically using the proposed steepest decent method. The related simulation results show that the proposed fuzzy controller is more powerful than the conventional ones.

• Proceedings of the KIEE Conference
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• 1995.07a
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• pp.385-387
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• 1995

The current mode controlled DC/DC converter using fuzzy logic controller is proposed. With the proposed control method, the robust and safty guaranteed operation are achieved. For comparison with conventional controller, the PI controller is selected. By the computer simulation results, the validities of the proposed control method will be shown.

• Journal of Industrial Technology
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• v.20 no.B
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• pp.155-162
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• 2000

Although Fuzzy Logic Controller(FLC) adopted three terms as input gives better performance, FLC is in general composed of two-term control because of the difficulty in the construction of fuzzy rule base. In this paper, a three-term FLC which is similar to PID control but acts as a nonlinear controller is proposed. To reduce the complexity of the rule base design and to increase efficiency. a simplified fuzzy PID control is induced from a hybrid velocity/position type PID algorithm by sharing a common rule base for both fuzzy PI and fuzzy PD parts. It is simple in structure, easy in implementation, and fast in calculation. The phase plane technique is applied to obtain the rule base for fuzzy two-term control and the resultant rule base is Macvicar-Whelan type. And the membership function is a Gaussian function. The frequency response information is used in tuning of the membership functions. Also a tuning strategy for the scaling factors is proposed based on the relationship between PID gain and the scaling factors. Simulation results show better performance and the effectiveness of the proposed method.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.10
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• pp.1529-1535
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• 2012

This paper presents the application algorithm for speed control of Switched Reluctance Motor. The conventional PI controller has been widely used in industrial applications. But it is very difficult to find the optimal PI control gain. Fuzzy control does not need any model of plant. It is based on plant operator experience and heuristics. The proposed fuzzy logic modifier increases the control performance of conventional PI controller. Simulation and experimental results show that the proposed fuzzy control method was superior to the conventional PI controller in the respect of system performance. The experiments are performed to verify the capability of proposed control method on 6/4 salient type SRM.