• Proceedings of the KIEE Conference
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• 1995.11a
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• pp.189-191
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• 1995

This paper proposes a design of self-tuning fuzzy controller based on evolutionary computation. Optimal membership functions are found by using evolutionary computation. Genetic algorithms and evolution strategy are used for tuning of fuzzy membership function. An arbitrarily speed trajectory is selected to show the performance of the proposed methods. Experiment results show the good performance in the DC motor control system with the self-tuning fuzzy controller based on evolutionary computation.

• Proceedings of the KIEE Conference
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• 2006.07b
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• pp.759-760
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• 2006

In this paper proposed to Neural network PI self-tuning direct controller using Error back propagation algorithm. Proposed controller applies to speed controller and current controller. Also, this built up the interface environment to drive it simply and exactly in any kind of reference, environment fluent and parameter transaction of BLDC motor. Neural network PI self-tuning simulator using Visual C++ and Matlab Simulation is organized to construct this environment. Built-u-p interface has it's own purpose that even the user who don't have the accurate knowledge of neural network can embody operation characteristic rapidly and easily.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.23 no.2
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• pp.175-181
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• 2009

In this paper, it is suggested that the selection method of parameter of Power System Stabilizer(PSS) with robustness in low frequency oscillation for Static VAR Compensator(SVC) using a self tuning fuzzy controller for a synchronous generator excitation and SVC system. 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.

• Proceedings of the KIEE Conference
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• 2005.10a
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• pp.136-138
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• 2005

The conventional self-tuning methods have the speed control problem of nonlinear BLDC motor which can't adapt against any kinds of noise or operation circumstances. In this paper, supposed to solve these problem to PI parameters controller algorithm using ANN. In the proposed algorithm, the parameters of the controller were adjusted to reduce by on-line system the error of the speed of BLDC motor. In this process, EBPA NN was constituted to an output error value of a BLDC motor and conspired an input and output. The performance of the self-tuning controller is compared with that of the PI controller tuned by conventional method(Z&N). The effectiveness of the proposed control method IS verified thought the Matlab Simulink.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.5
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• pp.29-38
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• 1998

This paper proposes the neural network self-tuning PID controller for the load frequency control of 2- areas power system, namely, the prompt convergence of frequency and tie-line power flow deviation. The neural network applied to computer simulation consists of neurons of two inputs, ten hiddens and tliree outputs layer. Neurons of two inputs layer receive the error and its change rate of the system and cutputs layer consists of three neurons for the parameters of the PID controller. The simulation results shows that the proposed neural network self-tuning PID controller is superior to conventional control t~:chniques(Optimal, PID) in dynamic response and control performance.

• 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.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.3
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• pp.246-254
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• 2002

In this paper auto-tuning scheme of PID controller based on the reference model has been studied for a Process control system by immune algorithm. Up to this time, many sophisticated tuning algorithms have been tried in order to improve the PID controller performance under such difficult conditions. Also, a number of approaches have been proposed to implement mixed control structures that combine a PID controller with fuzzy logic. However, in the actual plant, they are manually tuned through a trial and error procedure, and the derivative action is switched off. Therefore, it is difficult to tune. Since the immune 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. Simulation results reveal that reference model basd tuning by immune network suggested in this paper is an effective approach to search for optimal or near optimal process control.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.48.5-48
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• 2002

$\textbullet$ Contents 1 Introduction $\textbullet$ Contents 2 Crane Description $\textbullet$ Contents 3 Self-tuning Controller Design $\textbullet$ Contents 4 Result of Experiments $\textbullet$ Contents 5 Conclusions $\textbullet$ Contents 6 References

• Journal of Ocean Engineering and Technology
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• v.3 no.2
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• pp.551-551
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• 1989

In general, a remotely operated vehicle(ROV) operates at deep sea. The control system of ROV is composed of two local loops; the first loop placed on the surface vessel monitors and manipulates the attitude of the ROV using joystick, and the second part on the ROV automatically controls thrusters and acquires positional data. This paper presents a position control simulation of a ROV using an adaptive controller and discusses the control effects of two different conditions. The design of an adaptive control system is obtained by the application of a self-tuning controller with the minimization of an appropriate cost function. The parameters of the control system are estimated by a recursive least square method(RLS). In the simulation, a Runge-Kutta method is used for the numerical integration and the generated outputs are obtained by adding measurement errors. Additionally, this paper discusses the mathematical modelling of a ROV and make a survey of control systems.

• Journal of Ocean Engineering and Technology
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• v.3 no.2
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• pp.51-58
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• 1989

In general, a remotely operated vehicle(ROV) operates at deep sea. The control system of ROV is composed of two local loops; the first loop placed on the surface vessel monitors and manipulates the attitude of the ROV using joystick, and the second part on the ROV automatically controls thrusters and acquires positional data. This paper presents a position control simulation of a ROV using an adaptive controller and discusses the control effects of two different conditions. The design of an adaptive control system is obtained by the application of a self-tuning controller with the minimization of an appropriate cost function. The parameters of the control system are estimated by a recursive least square method(RLS). In the simulation, a Runge-Kutta method is used for the numerical integration and the generated outputs are obtained by adding measurement errors. Additionally, this paper discusses the mathematical modelling of a ROV and make a survey of control systems.