• Proceedings of the KIEE Conference
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• 1996.07a
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• pp.420-425
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• 1996

In this paper a robust speed controller for an induction motor is proposed. The speed controller consists or a fuzzy sliding adaptive controller(FSAC) and a sliding mode torque observer(SMTO). FSAC removes the problem or oscillations caused by discontinuous inputs of the sliding mode controller. The controller also provides robust characteristics against parameter and sampling time variations. Although, however, the performance of FSAC is better than PI controller and fuzzy controller in robustness, it generates the problem of slow response time. To alleviate this problem, a compensator, which performs feedforward control using torque signals produced by SMTO, is added. The simulation and hardware implementation results show that the proposed system is robust to the load disturbance, parameter variations, and measurement noises.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 1992.11a
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• pp.41-46
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• 1992

고전적인 제어 기법들을 이용한 전동기의 속도 제어기는 하나의 고정된 동작점에 대해서 대개 양호한 동작 특성을 얻을 수 있으나 전동기 매개변수의 섭동 및 부하 외란의 존재시 규정된 제어 동작을 유지하기가 어렵다는 단점을 갖고 있다. 따라서 본 연구에서는 이러한 단점을 극복하기 위하여 적응 제어 기법중의 하나인 기준 모델 적응 제어 (Model Reference Adaptive Control : MRAC) 방식을 직류 전동기의 속도 제어에 적용하였으며 또한, 2차 이상인 전동기의 속도 제어 시스템을 1차로 저차화시켜 제어 알고리즘의 계산에 소요되는 시간을 줄임으로써 실시간 제어가 가능토록 하였다. 제시된 기준 모델 제어 기법과 PI 제어 기법을 직류 전동기의 속도 제어에 각각 적용하고 부하의 관성변화에 다른 속도 응답 특성을 실험을 통하여 비교 검토하였다.

• 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.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.44 no.2 s.314
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• pp.24-31
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• 2007

This paper presents self tuning PI(STPI) controller of IPMSM drive using neural network. In general, PI controller in computer numerically controlled machine process fixed gain. They may perform well under some operating conditions, but not all. To increase the robustness of fixed gain PI controller, STPI controller proposes a new method based neural network. STPI controller is developed to minimize overshoot, rise time and settling time following sudden parameter changes such as speed, load torque and inertia. Also, this paper is proposed speed control of IPMSM using neural network and estimation of speed using artificial neural network(ANN) controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The results on a speed controller of IPMSM are presented to show the effectiveness of the proposed gain tuner. And this controller is better than the fixed gains one in terms of robustness, even under great variations of operating conditions and load disturbance.

• Korean Chemical Engineering Research
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• v.45 no.1
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• pp.12-16
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• 2007

Temperature control of an autothermal methanol reforming reactor which uses the copper-zinc oxide catalyst was studied. Temperature at 1cm below the hot-spot point in the reactor was used for the controlled variable, and the air flow rate was used for the manipulated variable. A first order plus time delay model was identified and controller parameters were obtained by applying the IMC-PI tuning rule to the identified model. With this controller, we could control the reforming reactor temperature within ${\pm}5^{\circ}C$ over 100 hours. Change of the hot-spot point due to the catalyst degradation was investigated and it could be used to design an adaptive 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.

• 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.

• 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.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.2
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• pp.146-156
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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.

• Proceedings of the KIPE Conference
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• 1998.07a
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• pp.85-89
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• 1998

A switching power stage is a very nonlinear system because it has two or more operation modes in one switching cycle. To model a switching power stage, the state space averaging method has been developed. Though it allows a unified treatment of a large variety of switching power stages, the model it yields is always very nonlinear. So, it is required to linearize the averaged model. But it is well known that a controller for a nonlinear plant designed by the linearization frequently fails in showing satisfactory control performance. Hence it is very natural to try to design a nonlinear controller for a switching power stage. In design of a switching power system, nonlinear control approaches such as adaptive control and fuzzy control have been widely studied so far. In this research, a recently developed control method, time delay control is briefly studied and a design example for a ZVS PWM half bridge converter is given. The performance of the time delay controller is compared to its conventional counterpart, PI controller by computer simulations.