• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.786-789
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• 1989

A nonlinear self-tuning regulator for a neutralization process of a weak acid and strong base system is proposed. Rearranging the state equation of the process model, we first obtain equations which are linear for a manipulated variable or unknown parameters. Then to these equations we apply the standard procedure used in designing linear self-tuning regulators. Simulation results show that the regulator provides very good performances for various realistic situations and traces variations of the unknown parameters. Since computations are simple and additional measurements except the effluent pH value are only flow rates of influent streams, it can be easily applied to real processes such as a waste water treatment process.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.505-505
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• 2000

Controlling the process by PID controller is widely used in industry by applying Ziegler-Nichols method in analyzing parameter of the controller. However, in fact. it is still necessary to tune parameter in order to obtain the best process response. This paper presents a Self-Tuning PID controller utilizes the personal computer to synthesize and analyze controller parameter as well as tune for appropriate parameter by using Dahlin method and Extrapolation. Experimental results using a Self-Tuning PID controller to control water level and temperature, it is found that the controller being developed is able to control the process very effectively and provides a good response similar to the controller used in the industry.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.114-114
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• 2000

In this paper a self-tuning modified skyhook control for the semi-active suspension systems is investigated. The damping force generation mechanism is modeled We consider a 2 DOF time-varying quarter car model that permits parameter variations of the sprung mass and suspension spring coefficient. The modified skyhook control algorithm proposed in this paper requires only the measurement of body acceleration. The absolute velocity of the sprung mass and the relative velocity of the suspension deflection are estimated by using integral filters, according to parameter variations. The skyhook gains are designed in such a way that the body acceleration and the dynamic tire force are optimized. An ECU prototype will be discussed

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.592-594
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• 1998

In the single-input and single-output system, the parameter of plant is scalar polynomial, but in the multiple input and multiple output, it accompanies, being matrix polynomial, the consideration of observable conrolability index or problems of non-commutation in matrix polynomial as well as degree, and it is more complex to deal with. Therefore, it is thought that a full reserach on the single-input and single-output system is not made. This reserach propose that problems of minimum variance self-tuning regulator of multivariable system and pole placement self-tuning regulator.

• 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.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.9
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• pp.739-744
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• 2000

A robust control algorithm under disturbance and reference change is developed using a self tuning control method incorporting of the well known internal model principle and the annihilator polynomical. The types of disturbance and reference signal are assumed to be given as known difference polynomials. The algorithm is shown for a minimum phase system with parameters of unknown parameters.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.244-249
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• 1998

In this paper, the hybrid state space self-tuning control technique Is studied within the framework of fuzzy systems and dual-rate sampling control theory. We show that fuzzy modeling techniques can be used to formulate chaotic dynamical systems. Then, we develop the hybrid state space self-tuning fuzzy control techniques with dual-rate sampling for digital control of chaotic systems. An equivalent fast-rate discrete-time state-space model of the continuous-time system is constructed by using fuzzy inference systems. To obtain the continuous-time optimal state feedback gains, the constructed discrete-time fuzzy system is converted into a continuous-time system. The developed optimal continuous-time control law is then convened into an equivalent slow-rate digital control law using the proposed digital redesign method. The proposed technique enables us to systematically and effective]y carry out framework for modeling and control of chaotic systems. The proposed method has been successfully applied for controlling the chaotic trajectories of Chua's circuit.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.51 no.12
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• pp.691-696
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• 2002

This paper presents a neural network based self-tuning fuzzy PID control scheme with variable learning rate for induction motor speed control. 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. Proposed scheme is simple in structure and computational burden is small. The simulation using Matlab/Simulink and the experiment using dSPACE(DS1102) board are performed to verify the effectiveness of the proposed scheme.

• 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 KIPE Conference
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• 2007.07a
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• pp.394-396
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• 2007

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 PMSM. Neural network PI self-tuning simulator using Visual C++ and Matlab Simulation is organized to construct this environment, Built up 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.