• Proceedings of the KIEE Conference
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• 2002.07b
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• pp.1165-1168
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• 2002

This paper presents a neural network based self-tuning fuzzy PID control scheme with variable learning rate for sensorless vector controlled induction motor drives. MRAS(Model Reference Adaptive System) is used for rotor speed estimation. When induction motor is continuously used long time. its electrical and mechanical parameters will change, which degrade the performance of PID controller considerably. This paper re-analyzes the fuzzy controller as conventional PID controller structure, introduces a single neuron with a back-propagation learning algorithm to tune the control parameters, and proposes a variable learning rate to improve the control performance. The proposed scheme is simple in structure and computational burden is small. The simulation using Matlab/Simulink and the experiment using DS1102 board show the robustness of the proposed controller to parameter variations.

• Proceedings of the KIEE Conference
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• 1994.11a
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• pp.338-341
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• 1994

This paper presents a Fuzzy-PID controller based on Fuzzy logic. Up to now PID controller has had the difficulty of obtaining the optimal gain, and Fuzzy controller has had the difficulty of determining scale factor affecting the performance of control. So that a Fuzzy-PID controller is presented here self tuning of the scale factor and optimal gain. The results of simulation show a good performance in comparison with Ziegler-Nichols controller, having the generality of determining the components of scale factor in Fuzzy rule.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.682-684
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• 1995

The most effective way to improve the performance of a fuzzy controller may be to optimize look-up values. Look-up values are derived from processes used input-output scale factors, membership functions, rule base, fuzzy inference method and defuzzification. It is powerful way to modify or organize look-up table values. In this paper, We propose the look-up values self-organizing fuzzy controller(LSOFC). We use the plus-minus tuning method(PMTM), scanning values through the processes of addition and subtraction. We show the efficiency of this LSOFC by the results of simulation for nonlinear time-varying plant with unmodelled dynamics.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.825-829
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• 2005

This paper presents an approach for designing a fuzzy logic-based adaptive SVC damping In controller for damping low frequency power oscillations. Power systems are often subject to low Frequency electro-mechanical oscillations resulting from electrical disturbances. Generally, power system stabilizers are designed to provide damping against this kind of oscillations. Another means to achieve damping is to design supplementary damping controllers that are equipped with SVC. Various approaches are available for designing such controllers, many of which are based on the concepts of damping torque and others which treat the damping controller design as a generic control problem and apply various control theories on it. In our proposed approach, linear optimal controllers are designed and then a fuzzy logic tuning mechanism is constructed to generate a single control signal. The controller uses the system operating condition and a fuzzy logic signal tuner to blend the control signals generated by two linear controllers, which are designed using an optimal control method. First, we design damping controllers for the two extreme conditions; the control action for intermediate conditions is determined by the fuzzy logic tuner. The more the operating condition belongs to one of the two fuzzy sets, the stronger the contribution of the control signal from that set in the output signal. Simulation studies done on a one-machine infinite-bus and a four-machine two-area test system, show that the proposed fuzzy adaptive damping SVC controller effectively enhances the damping of low frequency oscillations.

• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.379-382
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• 1997

We present a hybrid self-tuning method using hyper elliptic Gaussian membership function. The proposed method applies a GA to identify the structure and the parameters of a fuzzy inference system. The parameters obtained by a GA, however, are near optimal solutions. So we solve this problem through a backpropagation-type gradient method. It is called GA hybrid self-tuning method in this paper. We provide a numerical example to evaluate the advantage and effectiveness of the proposed approach and compare with the conventional method.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.684-688
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• 1996

In this paper, to overcome drawbacks of variable structure control system a self-tuning fuzzy sliding mode control algorithm using gradient descent method is proposed. The proposed method has the characteristics which are viewed in conventional VSC, e.g. insensitivity to a class of disturbance, parameter variations and uncertainties in the sliding mode. To demonstrate its performance, the proposed control algorithm is applied to a one-degree of freedom robot arm. The results show that both alleviation of chattering and performance are achieved.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.4
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• pp.329-334
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• 2007

This paper deals with self-tuning of fuzzy control systems. The fuzzy logic controller(FLC) has parameters that an: input and output scaling factors to effect control output. Tuning method is proposed for the scaling factor. In this paper. it is studied to control and to monitor the remote system statues using SOAP for communicate between the server part and the client part. The remote control system is controlled by using a web browser or a application program. The server part is waiting for the request of client part that uses internet network for communication each other and then the request is reached. the server part saves client data to the database and send a command set to the client part and then the client part sends command to controller in a cool chamber. The administrator can control and monitor the remote system just using a web browser. The effects of membership functions, defuzzification methods and scaling factors are investigated in the FLC system.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.3
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• pp.261-268
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• 2002

This paper represents that auto tunings of membership functions and weights in the fuzzy neural networks are effectively performed by immune algorithm. A number of hybrid methods in fuzzy-neural networks are considered in the context of tuning of learning method, a general view is provided that they are the special cases of either the membership functions or the gain modification in the neural networks by genetic algorithms. On the other hand, since the immune network system possesses a self organizing and distributed memory, it is thus adaptive to its external environment and allows a PDP (parallel distributed processing) network to complete patterns against the environmental situation. Also, it can provide optimal solution. Simulation results reveal that immune algorithms are effective approaches to search for optimal or near optimal fuzzy rules and weights.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1992.10a
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• pp.182-186
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• 1992

A self tuning technique is derived for PID controllers which are widely used in industries. The tuning algorithm is based upon a fuzzy indirect reasoning method and an iterative technique. The fuzzy technique is considered to obtain ease and simplicity of tuning process. The PID gains for the first tuning action are determined by a method which is modified from the Ziegler-Nichols step response method. The first PID gains are determined to obtain a control performance so close to a design performance that the followed tuning process can be made effectively. The design parameters are given as time-domain variables which human is familiar with. The results of simulation studies show that the proped tuning method can produce an effective tuning for arbitaray design performances.

• Journal of Power Electronics
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• v.13 no.4
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• pp.656-668
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• 2013

This paper presents a wavelet-fuzzy based controller for indirect field oriented control of three-phase induction motor drives. The discrete wavelet transform is used to decompose the error between the actual speed and the command speed of the induction motor drive into different frequency components. The transformed error coefficients along with the scaling gains are used for generating the control component of the motor. Self-tuning fuzzy logic is used for online tuning of the scaling gains of the controller. The proposed controller has the ability to meet the speed tracking requirements in the closed loop system. The complete indirect field oriented control scheme incorporating the proposed wavelet-fuzzy based controller is investigated theoretically and simulated under various dynamic operating conditions. The simulation results are compared with a conventional proportional integral controller and a fuzzy based controller. The speed control scheme incorporating the proposed controller is implemented in real time using a digital processor control board. Simulation and experimental results validate the effectiveness of the proposed controller.