• Proceedings of the KIEE Conference
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• 2002.06a
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• pp.60-63
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• 2002

This paper presents a control approach for designing a self tuning fuzzy controller for SVC system, A SVC constructed by a Fixed Capacitor and a Thyristor Controlled Reactor is designed and implemented to improve the damping 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, The related simulation results show that the proposed fuzzy controller is more powerful than the conventional ones.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.449-452
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• 1998

In this paper, we implemented the variable fuzzy speed controller of an IM(induction motor) using the fuzzy control algorithms. Specially, we proposed a self-tuning technique of scale factors which could make easily the fuzzy speed controller optimize. Comparing with the conventional PI speed controller, the dynamic performances of a proposed fuzzy controller such as the reaching time, the maximum overshoot and the robustness against load disturbance were substantially improved.

• The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.9 no.6
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• pp.71-77
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• 1995

This paper deals with design method of learning fuzzy controller for control of an unknown nonlinear plant using the self-tuning algorithm of fuzzy inference rules. In this method the fuzzy identification model obtained that the joined identification model of nonlinear part and linear identification model of linear part by fuzzy inference systems. This fuzzy identification model ordered self-tuning by Decent method so as to be servile to nonlinear plant. A the end, designed learning fuzzy controller of fuzzy identification model have learning structure to model reference adaptive system. The simulation results show that th suggested identification and learning control schemes are practically feasible and effective.

• Proceedings of the KIEE Conference
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• 2001.07b
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• pp.1182-1184
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• 2001

This paper presents a neural network based self-tuning fuzzy PID control scheme for induction motor speed control. 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 PID controller whose scaling factors are adjusted automatically. Proposed scheme is simple in structure and computational burden is small. The simulation using Matlab/Simulink is performed to verify the effectiveness of the proposed scheme.

• 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.

• Journal of the Korean Society of Industry Convergence
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• v.3 no.1
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• pp.3-8
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• 2000

This paper describes a development of self tuning scheme for PI and PO type fuzzy controllers. The output scaling factor(SF) is adjusted on-line by fuzzy rules according to the current trend of the controlled process. The rule-base for tuning the output SF is defined on error and change of error for the controlled variable using the most natural and unbiased membership functions. Simulation results demonstrate the better control performance can be achieved in comparison with Ziegler-Nichols(Z-N) PID controllers.

• The Transactions of the Korean Institute of Power Electronics
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• v.3 no.4
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• pp.388-398
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• 1998

In constructing fuzzy control systems. there are many parameters such as rule base. membership functions. inference m method. defuzzification. and I/O scaling factors. To control the system in properly using fuzzy logic. we have to consider t the correlation with those parameters. This paper deals with self-tuning of fuzzy control systems. The fuzzy controller h has parameters that are input and output scaling factors to effect control output. And we propose the looklongleftarrowup table b based self-tuning fuzy controller. We propose the PMTM(Plus-Minus Tuning Method) for self tuning method, self-tuning the initial look-up table to the appropriate table by adding and subtracting the values.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.127-130
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• 1995

Fuzzy controllers have proven to be powerful in controlling dynamic processes where mathematical models are unknown or intractable and ill-defined. The way of improving the performance of a fuzzy controller is based on making up rules, constructing membership functions, selecting a defuzzification method and adjusting input-output scaling factors. But there are many difficulties in tuning those to optimize a fuzzy controller. So, in this paper, we propose the look-up table based self-orgenizing fuzzy controller (LSOFC) which optimizes look-up values resulting from the above fuzzy processes. We use the plus-minus tuning method(PMTM), scanning the value through the processes of addition and subtraction. Simulation results demonstrate that the performance of LSOFC is far better than that of a non-tuning fuzzy controller.

• Proceedings of the KIEE Conference
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• 2006.04a
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• pp.210-212
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• 2006

This paper presents Hybrid PI(HBPI) controller of IPMSM drive using fuzzy adaptive mechanism control. In general, PI controller in computer numerically controlled machine process fixed gain. They may perform well under some operating conditions, but not all. To increase the robustness, fixed gain PI controller, HBPI controller proposes a new method based self tuning PI controller. HBPI controller is developed to minimize overshoot and settling time following sudden parameter changes such as speed, load torque, inertia, rotor resistance and self inductance. The results on a speed controller of IPMSM are presented to show the effectiveness of the proposed gain tuner. And this controller is better than the fixed gains one in terms of robustness, even under great variations of operating conditions and load disturbance.

• 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.