• Journal of the Institute of Electronics Engineers of Korea SC
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• v.39 no.6
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• pp.1-8
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• 2002

This paper presents a generalized minimum-variance self-tuning controller with a PID structure using neural network which adapts to the changing parameters of the nonlinear system with nonminimum phase behavior and time delays. The neural network is used to estimate the controller parameters, and the control output is obtained through estimated controller parameter. In order to demonstrate the effectiveness of the proposed algorithm, the computer simulation is done to adapt the nonlinear nonminimum phase system with time delays and changed system parameter after a constant time. The proposed method compared with direct adaptive controller using neural network.

• Proceedings of the KIEE Conference
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• 2003.07b
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• pp.1111-1113
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• 2003

This paper proposes an effective self-turning algorithm based on Artificial Neural Network (ANN) for fuzzy speed control of the indirect vector controlled induction motor. Indirect vector control method divides and controls stator current by the flux and the torque producing current so that the dynamic characteristic of induction motor may be superior. However, if motor parameter changes, the flux current and the torque producing one's coupling happens and deteriorates the dynamic characteristic. The fuzzy speed controller of an induction motor has the robustness over the effect of this parameter variation than a conventional PI speed controller in some degree. This paper improves its adaptability by adding the self-tuning mechanism to the fuzzy controller. For tracking the speed command, its membership functions are adjusted using ANN adaptation mechanism. This adaptability could be embodied by moving the center positions of the membership functions. Proposed self-tuning method has wide adaptability than existent fuzzy controller or PI controller and is proved robust about parameter variation through Matlab/Simulink simulation.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.13-17
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• 1993

In this paper, a design method of controller which incorporates pole restriction into implicit self tuning algorithm is proposed. The idea behind pole restriction is that the closed loop poles of the system are restricted to a user-chosen circle in the region to meet maximum percentage overshoot and settling time specification. Most algorithm based on pole restriction are explicit schemes involving a parameter estimation and synthesis stage to obtain controller parameters. The object of this paper is to have an algorithm that has the idea of pole restriction and the simplicity of the implicit approach.

• Proceedings of the KIEE Conference
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• 1988.11a
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• pp.430-433
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• 1988

An adaptive control system for D.C servo drive is developed via minimum variance control theory. The problem of designing this controller under varying load conditions is discussed. A robust self tuning controller that can track a constant reference and reject constant load disturbance is developed. Simulation study shows that the controller has excellent adaptation, capability as well as transient recovery under load changes.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.11a
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• pp.236-242
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• 1996

The purpose of this paper is to study the tension control in a web transport system. Direct self-tuning regulator method was applied to tension controller and variable-gain PID control algorithm was applied to web speed controller. The designed controllers compensated for the time-varying parameters and tracked reference tension in process speed changing. The simulation shows that direct STR tension controller improves tension control performance in comparison with other controllers.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.229-229
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• 2000

Conventional gain-tuning methods such as Ziegler-Nickels methods, have many disadvantages that optimal control ler gain should be tuned manually. In this paper, modified PID controllers which include self-tuning characteristics are proposed. Proposed controllers automatically tune the PID gains in on-1ine using neural networks. A new learning scheme was proposed for improving learning speed in neural networks and satisfying the real time condition. In this paper, using a nonlinear mapping capability of neural networks, we derive a tuning method of PID controller based on a Back propagation(BP)method of multilayered neural networks. Simulated and experimental results show that the proposed method can give the appropriate parameters of PID controller when it is implemented to DC Motor.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2651-2654
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• 2000

In this paper, we re-analyzed the fuzzy controller as conventional PID controller structure, and proposed a self tuning fuzzy PID controller whose input output scaling factors were tuned automatically. At first stage, the tuning parameters of fuzzy controller were determined by Ziegler-Nichols tuning method and then they were adjusted as the delay time and process environment were changed. Proposed controller was simple in its structrue and computational burden was small so that on line adaptation was easy to apply to. The result of computer simulation and practical experiment showed the proposed controller's excellent performance

• International Journal of Ocean System Engineering
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• v.3 no.1
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• pp.16-21
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• 2013

This paper presents a design of fuzzy PD depth controller for the autonomous underwater vehicle entitled KAUV-1. The vehicle is shaped like a torpedo with light weight and small size and used for marine exploration and monitoring. The KAUV-1 has a unique ducted propeller located at aft end with yawing actuation acting as a rudder. For depth control, the KAUV-1 uses a mass shifter mechanism to change its center of gravity, consequently, can control pitch angle and depth of the vehicle. A design of classical PD depth controller for the KAUV-1 was presented and analyzed. However, it has inherent drawback of gains, which is their values are fixed. Meanwhile, in different operation modes, vehicle dynamics might have different effects on the behavior of the vehicle. In this reason, control gains need to be appropriately changed according to vehicle operating states for better performance. This paper presents a self-tuning gain for depth controller using the fuzzy logic method which is based on the classical PD controller. The self-tuning gains are outputs of fuzzy logic blocks. The performance of the self-tuning gain controller is simulated using Matlab/Simulink and is compared with that of the classical PD controller.

• Journal of Institute of Control, Robotics and Systems
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• v.2 no.1
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• pp.14-20
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• 1996

This paper presents a self tuning method of a velocity type PID controller for minimum or non-minimum phase systems with time delays. The velocity type PID control structure is determined in the process of minimizing the variance of the auxilliary output, and self tuning effect is achieved through the recursive least square algorithm at the parameter estimation stage and also through the Robbins-Monro algorithm at the stage of optimizing a design parameter. This method is simple and effective compared with other existing methods[1,2]. Numerical examples are included to illustrate the procedure and to show the performance of the control system.

• 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.