• 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.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.178.4-178
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• 2001

In this paper a self-tuning gain-scheduled skyhook control for semi-active suspension systems is investigated. The dynamic characteristics of a continuously variable damper including electro-hydraulic pressure control valves is analyzed. A 2-d.o.f. time-varying quarter-car model that permits variations in sprung mass and suspension spring coefficient is considered. The self-tuning 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. The skyhook gains are gain-scheduled in such a way that the body acceleration and the dynamic tire force are optimized. An ECU prototype ...

• 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.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.652-654
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• 1995

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

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.2
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• pp.160-166
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• 2005

In this paper, to overcome drawbacks of FLC a self tuning fuzzy sliding mode controller is proposed, which controls the position of excavator's attachment, which can be regarded as an ill-defined system. It is reported that fuzzy logic theory is especially useful in the control of ill-defined system. It is important in the design of a FLC to derive control rules in which the system's dynamic characteristics are taken into account. Control rules are usually established using trial and error methods. However, in the case where the dynamic characteristics vary with operating conditions, as in the operation of excavator attachment, it is difficult to find out control rules in which all the working condition parameters are considered. Experiments are carried out on a test bed which is built around a commercial Hyundai HX-60W hydraulic excavator. The experimental results show that both alleviation of chattering and performance are achieved. Fuzzy rules are easily obtained by using the proposed method and good performance in the following the desired trajectory is achieved. In summary, the proposed controller is very effective control method for the position control of the excavator's attachment.

• 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.

• 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.

• Nuclear Engineering and Technology
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• v.27 no.3
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• pp.317-326
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• 1995

The oater level control system of the steam generator in a pressurized water reactor and its control Problems are analysed. In this work a stable control strategy Particularly during low Power operation based on the fuzzy control method is studied. The control strategy employs substitutional information using the bypass valve opening instead of incorrectly measured signal at the low How rate as the fuzzy variable of the flow rate during low power operation, and includes the flexible scale adjusting method for fast response at a large transient. A self-tuning algorithm based on the control performance and the descent method is also suggested for tuning the membership function scale. It gives a practical way to tune the controller under real operation. Simulation was carried out on the Compact Nuclear Simulator set up at Korea Atomic Energy Research Institute and its result showed the good performance of the controller and effectiveness of its tuning.

• 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.