• Journal of Power Electronics
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• v.10 no.3
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• pp.306-312
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• 2010

In this paper, the design of an optimum single input Fuzzy controller for application in dc to ac converters is presented. Contrary to conventional Fuzzy controllers, the proposed controller has a smaller number of rules and tuning parameters but is capable of performing identically to a conventional controller. These benefits lead to a simpler controller design. The controller is designed as a PI controller for small-signal disturbances. However, for optimum large-signal performance, heuristic tuning is used. The tuning is less complicated and hence optimum large-signal performance is achievable. The system is simulated and a hardware prototype was developed for comparison purposes.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.704-707
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• 1997

In this paper, we present a fuzzy logic based tuner for continuous on-line tuning of PID controllers. The essential idea of the scheme is to parameterize a Ziegler-Nichols-like tuning formula by a singler parameter .alpha., then to use an on line fuzzy logic to self-tune the parameter. The adaptive scaling makes the controller robust against large variations in parametric and dynamics uncertainties in the plant model. New self-tuning controller has the ability to decide when to use PI or PID control by extracting process dynamics from relay experiments. These scheme lead to improved performance of the transient and steady state behavior of the closed loop system, including processes with nonminimum phase processes.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.1929-1931
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• 2001

An adaptive fuzzy logic controller to regulate molten steel level in the strip casting process is presented, where parameters of fuzzy controllers are adapted stably by using Lyapunov-stability theory and a switching controller is used together to deal with the approximation error of fuzzy logic system. The level error is proven to converge to zero asymptotically. In the simulation, the clogging/unclogging of a stopper nozzle is considered and overcome by the proposed controller. Robustness to uncertainty is shown to be superior to conventional PI controller.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2009.05a
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• pp.404-407
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• 2009

The field oriented control of induction motors is widely used in high performance applications. However, detuning caused by parameter disturbance still limits the performance of these drives. In order to accomplish variable speed operation conventional PI-like controllers are commonly used. These controllers provide limited good performance over a wide range of operation even under ideal field oriented conditions. This paper is proposed adaptive fuzzy controller(AFC) and artificial neural network(ANN) based on the vector controlled induction motor drive system. Also, this paper is proposed control of speed and current using fuzzy adaptation mechanism(FAM), AFC and estimation of speed using ANN. The proposed control algorithm is applied to induction motor drive system using FAM, AFC and ANN controller. Also, this paper is proposed the analysis results to verify the effectiveness of this controller.

• Proceedings of the KIEE Conference
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• 2009.04b
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• pp.32-34
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• 2009

The field oriented control of induction motors is widely used in high performance applications. However, detuning caused by parameter disturbance still limits the performance of these drives. In order to accomplish variable speed operation, conventional PI-like controllers are commonly used. These controllers provide limited good Performance over a wide range of operation, even under ideal field oriented conditions. This paper is proposed model reference adaptive fuzzy control(MFC) and artificial neural network(ANN) based on the vector controlled induction motor drive system. Also, this paper is proposed control of speed and current using fuzzy adaption mechanism(FAM), MFC and estimation of speed using ANN. The proposed control algorithm is applied to induction motor drive system using FAM, MFC and ANN controller. Also, this paper is proposed the analysis results to verify the effectiveness of this controller.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2372-2377
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• 2003

Nonlinear dynamic system exist widely in many types of systems such as chemical processes, biomedical processes, and the main steam temperature control system of the thermal power plant. Up to the present time, PID Controllers have been used to operate these systems. However, it is very difficult to achieve an optimal PID gain with no experience, because of the interaction between loops and gain of the PID controller has to be manually tuned by trial and error. This paper suggests control approaches by immune fuzzy for the nonlinear control system inverted pendulum, through computer simulation. This paper defines relationship state variables $x,{\dot{x}},{\theta},\dot{\theta}$ using immune fuzzy and applied its results to stability.

• Nuclear Engineering and Technology
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• v.25 no.2
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• pp.222-232
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• 1993

In this paper, we present two sets of fuzzy algorithms for the steam generator level control ; one for the high power operations where the flow error is available and the other for the low power operations where the flow error is not available. These are converted to a PID type controller for the high power case and to a quadratic function form of a controller for the low power case. These controllers are implemented on the Compact Nuclear Simulator at Korea Atomic Energy Research Institute and tested by a set of four simulation experiments for each. For both cases, the results show that the total variation of the level error and of the flow error are about 50% of those by the PI controllers with about one half of the control action. For the high power case, this is mainly due to the fact that a combination of two PD type controllers in the velocity algorithm form rather than a combination of two PI type controllers in the position algorithm form is used. For the low power case, the controller is essentially a PID type with a very small integral component where the average values for the derivative component input and for the controller output are used.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.234-234
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• 2000

A direct torque control(DTC) based sensorless speed control system which employs a new closed loop flux observer is proposed. The flux observer takes an adaptive scheduling gains where motet speed is used as the scheduling variable. Adaptive nature comes from the fact that the estimated values of stator resistance and speed are included as observer parameters. The parameters of the PI controllers adopted in the adaptive law for the estimation of stator resistance and motor speed are determined by simple genetic algorithm. Simulation results in low speed region are given for comparison between proposed and conventional flux estimate scheme.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2009.05a
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• pp.416-419
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• 2009

The widely used control theory based design of PI family controllers fails to perform satisfactorily under-parameter variation, nonlinear or load disturbance. In high performance applications, it is useful to automatically extract the complex relation that represent the drive behaviour. The use of loaming through example algorithms can be a powerful tool for automatic modelling variable speed drives. They can automatically extract a functional relationship representative of the drive behavior. These methods present some advantages over the classical ones since they do not rely on the precise knowledge of mathematical models and parameters. The paper proposes high performance speed and current control of synchronous reluctance motor(SynRM) drive using adaptive loaming mechanism-fuzzy neural network (ALM-FNN) and fuzzy logic control(FLC) controller. The proposed controller is developed to ensure accurate speed and current control of SynRM drive under system disturbances and estimation of speed using artificial neural network(ANN) controller. Also, this paper proposes the analysis results to verify the effectiveness of the ALM-FNN and ANN controller.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.58 no.3
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• pp.249-256
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• 2009

The widely used control theory based design of PI family controllers fails to perform satisfactorily under parameter variation, nonlinear or load disturbance. In high performance applications, it is useful to automatically extract the complex relation that represent the drive behaviour. The use of learning through example algorithms can be a powerful tool for automatic modelling variable speed drives. They can automatically extract a functional relationship representative of the drive behavior. These methods present some advantages over the classical ones since they do not rely on the precise knowledge of mathematical models and parameters. The paper proposes high performance speed and current control of synchronous reluctance motor(SynRM) drive using adaptive learning mechanism-fuzzy neural network (ALM-FNN) and fuzzy logic control (FLC) controller. The proposed controller is developed to ensure accurate speed and current control of SynRM drive under system disturbances and estimation of speed using artificial neural network(ANN) controller. Also, this paper proposes the analysis results to verify the effectiveness of the ALM-FNN, FLC and ANN controller.