• 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 ...

• Computers and Concrete
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• v.30 no.5
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• pp.357-367
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• 2022

In this paper, we propose an efficient control method that can be transformed into a general building control problem for building structure control using these reliability criteria. To facilitate the calculation of controller H∞, an efficient solution method based on Linear Matrix Inequality (LMI) is introduced, namely H∞-based LMI control. In addition, a self-tuning predictive grey fuzzy controller is proposed to solve the problem caused by wrong parameter selection to eliminates the effect of dynamic coupling between degrees of freedom (DOF) in Self-Tuning Fuzzy Controllers. We prove stability using Lyapunov's stability theorem. To check the applicability of the proposed method, the proposed controller is applied and the control characteristics are determined. The simulation assumes system uncertainty in the controller design and emphasizes the use of acceleration feedback as a practical consideration. Simulation results show that the performance of the proposed controller is impressive, stable, and consistent with the performance of LMI-based methods. Therefore, an effective control method is suitable for seismic reinforcement of civil buildings.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.421-426
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• 2005

Fuzzy logic, neural network, fuzzy-neural network play an important as the key technology of linguistic modeling for intelligent control and decision making in complex systems. The fuzzy-neural network (FNN) learning represents one of the most effective algorithms to build such linguistic models. This paper proposes particle swarm optimization algorithm based optimal learning fuzzy-neural network (PSOA-FNN). The proposed learning scheme is the fuzzy-neural network structure which can handle linguistic knowledge as tuning membership function of fuzzy logic by particle swarm optimization algorithm. The learning algorithm of the PSOA-FNN is composed of two phases. The first phase is to find the initial membership functions of the fuzzy neural network model. In the second phase, particle swarm optimization algorithm is used for tuning of membership functions of the proposed model.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1716-1722
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• 2005

Fuzzy logic, neural network, fuzzy-neural network play an important as the key technology of linguistic modeling for intelligent control and decision in complex systems. The fuzzy-neural network (FNN) learning represents one of the most effective algorithms to build such linguistic models. This paper proposes bacteria foraging algorithm based optimal learning fuzzy-neural network (BA-FNN). The proposed learning scheme is the fuzzy-neural network structure which can handle linguistic knowledge as tuning membership function of fuzzy logic by bacteria foraging algorithm. The learning algorithm of the BA-FNN is composed of two phases. The first phase is to find the initial membership functions of the fuzzy neural network model. In the second phase, bacteria foraging algorithm is used for tuning of membership functions of the proposed model.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.3
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• pp.355-367
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• 2007

This paper proposes a design method of the PI(Proportional-Integral)+D(Derivative) controller using self-tuning of the design variables and controller gains. The used fuzzy PI+D controller is the approximated conventional continuos time linear PI+D controller and the used fuzzification method is the fuzzy single tone and the adapted defuzzification method is the simplified tenter of gravity. Fuzzy estimation result would be calculated in the other function elements from the classified fuzzy variables and the result determined by the design variables decides the controller gains. As a result, the proposed method shows the capability of the high speed tuning and can be applied to the case of input variables with many fuzzy partitions and also can bring out the advantage to reduce the reconstruction(digital sampling reconstruction) error. Most simulation results show that this controller makes much bettor efficiency and improvement by using design variables and controller gains.

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.2
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• pp.52-65
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• 1997

This paper presents a design technique of self tuning fuzzy controller for load frequency control of power system. The proposed parameter self tuning algorithm of fuzzy controller is based on the gradient method using four direction vectors which make error between inference values of fuzzy controller and output values of the specially selected optimal controller reduce steepestly. Using input-output data pair obtained from optimal controller, the parameters in antecedent part and in consequent part of fuzzy inference rules are learned and tuned automatically using the proposed gradient method. The related simulation results show that the proposed fuzzy controller is more powerful than the conventional ones for reductions of undershoot and steady-state load frequency deviation and for minimization of settling time.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.57 no.3
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• pp.244-249
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• 2008

This paper deals with speed control of DC servo motor using a Fuzzy tuning observer. Speed sensor detect a speed of rotor continuously. But it have a limit as a driving speed to detect speed precisely. So it is problem to improve the performance of the driving system. To solve the problem, it is studied to detect a speed of DC motor without sensor. In particular, study on the method to estimate the speed using the observer is performed a lot. In this parer, the gain of the observer is properly set up using the fuzzy observer. The fuzzy observer has a superior transient characteristic and is easy to implement compared the existing method is designed. It estimate the derivative of the armature current directly using the armature current measured in the DC motor. It estimate the speed of the rotor using the differentiation. It is proposed speed sensorless control method using the estimated speed. Optimal gain of Luenberger observer is set up using the fuzzy observer and adapted speed control of DC servo motor low speed operation. It is proved excellence and feasibility of the presented observer from the comparison tested a case with a speed sensor and a case without a speed sensor which used a highly efficient drive and 200W DC servo motor starting system.

• Proceedings of the IEEK Conference
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• 2003.07c
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• pp.2807-2810
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• 2003

In this paper, we propose a fuzzy control method for improving the control performance by automatically tuning the scaling factor. The proposed method is that automatically tune the input scaling factor and the output scaling factor of fuzzy logic system through neural network. Used neural network is ADALINE (ADAptive Linear NEron) neural network with delayed input. ADALINE neural network has simple construct, superior learning capacity and small computation time. In order to verify the effectiveness of the proposed control method, we performed simulation. The results showed that the proposed control method improves considerably on the environment of the disturbance.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.830-833
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• 1993

In this paper, an optimal tuning Algorithms is presented to automatically improve the performance of fuzzy controller, using the simplified reasoning method and the proposed complex method. The method estimates automatically the optimal values of the parameters of fuzzy controller, according to the change rate and limitation condition of output. The controller is applied to plants with dead time. Then, computer simulations are conducted at step input and the performances are evaluated in the ITAE.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2702-2704
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• 2001

In this Letter, we propose a comprehensive design methodology of hybri'd Fuzzy controllers (HFC). The HFC comes as a form of a convex combination of a standard PID controller and a fuzzy controller. The design procedure dwells on the use of evolutionary computing (genetic algorithm) and an auto-tuning algorithm. The tuning of the scaling factors of the HFC is an essential component of the entire optimization process. A numerical study is presented and a detailed comparative analysis is also included.