• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.525-530
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• 2003

This paper focuses on design of nonlinear power plant controller using immune based multiobjective fuzzy approach. The thermal power plant is typically regulated by the fuel flow rate, the spray flow rate, and the gas recirculation flow rate. However, Strictly maintaining the steam temperature can be difficult due to heating value variation to the fuel source, time delay changes in the main steam temperature. the change of the dynamic characteristics in the steam-turbine system. Up to the present time, PID Controller has been used to operate this system. However, it is very difficult to achieve an optimal PID gain with no experience, since the gain of the PID controller has to be manually tuned by trial and error. These parameters tuned by multiobjective based on immune network algorithms could be used for the tuning of nonlinear power plant.

• Proceedings of the KIEE Conference
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• 2000.07a
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• pp.34-36
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• 2000

This paper proposes the design of SVC adaptive fuzzy logic controller(AFLC) using Tabu search and neural network. We tuned the gain of input-output variables of fuzzy logic controller and weights of neural network using Tabu search. Neural network used to tune the output gain of FLC adaptively. We have weights of neural network learned using back propagation algorithm. We performed the nonlinear simulation on an single-machine infinite system to prove the efficiency of the proposed method. The proposed AFLC showed the better performance than PD controller in terms of the settling time and damping effect, for power system operation condition.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2158-2160
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• 2002

In this paper, we designed the adaptive fuzzy controller(AFLC) using neural network and tabu search. We tuned the weights of neural network changing adaptively input/output gain of fuzzy logic controller and the gain of fuzzy logic controller using tabu search. To evaluate the proposed method's effectiveness, we apply the proposed AFLC to the speed control of an actual AC servomotor system. The experimental results show that AFLC has the better control performance than PI controller in terms of settling time, rising time and overshoot.

• Proceedings of the KIEE Conference
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• 1998.07f
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• pp.2012-2014
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• 1998

The conventional PI controller has been widely used in industrial applications. If a PI control gain is selected suitable, the PI controller shows very good control performance. But it is very difficult to find the optimal PI control gain. Therefore, in this paper, the 4-rule based fuzzy logic modifier of the conventional PI controller are presented. The fuzzy logic modifier which exhibits a stabilizing effects on the closed-loop system, has good robustness regarding the improperly tuned PI controller. The simulation are performed to verify the capability of proposed control method on vector controlled induction motor drive system.

• Proceedings of the KIEE Conference
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• 1995.07a
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• pp.331-333
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• 1995

A high performance motor drive system must have a good speed command tracking, a insensitivity to a parameter variation and sampling time. In this paper, a robust speed controller for an induction motor is proposed. The speed controller is fuzzy-sliding adaptive controller and its system continuously is varied. That is, only P gain act in dynamic state, I gain in steady-state. Because this system is a sort of adaptive control system, global stability analysis is used to Lyapunov function. Consequently, in this paper application of fuzzy sliding adaptive controller to induction motor controlled by vecter control is presented and the control system is digitally implemented within DSP.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2345-2348
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• 2003

This paper deals with a problem of automatic landing guidance and control system design. The auto-landing control system for the longitudinal motion is designed in the classical PID controller. The controller gains are properly adapted to variation of the performance using fuzzy logic as a gain scheduler for the PID gains. This control logic is applied to the problem of the automatic landing control system design. From the numerical simulation using the 6DOF nonlinear model of the associated airplane, it is shown that the auto-landing maneuver is successfully achieved from the start of the flight conditions: 1500 ft altitude, 250 ft/sec airspeed and zero flight path angle.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.187-190
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• 1998

The values of physical components of the plants and controllers as well as the relevant environmental conditions change in time, thus the output performance can be deteriorated during the operating span of the system. Naturally the duty of calibration or the prevention of performance deterioration due to excessive component sensitivity should be provided to the control system. The proposed controller, whenever necessary, measures the open-loop and close-loop characteristics, and then calculates the offset and sensor gain correction values based on the prepared standard measurements It is applied to the control of a flexible link system with the gain and offset calibration problems in the light sensor module for position to show the applicability. In this paper, we propose a digital controller which has the capability of calibration gain and offset adjustment using fuzzy methods.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.25 no.2
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• pp.103-112
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• 2011

This paper proposes a new fuzzy speed controller for the precise speed control of a permanent magnet synchronous motor(PMSM). The proposed control system needs the information of the angular acceleration instead of the load torque, so the third-order fuzzy acceleration observer estimates it. Moreover, the LMI conditions are derived for the existence of the fuzzy acceleration observer and fuzzy speed controller, and the gain matrices of the observer and controller are obtained. It is analytically proven that the proposed observer-based fuzzy speed regulator is exponentially stable. To evaluate the performance of the proposed control algorithm, experimental results as well as simulation results are provided under the conditions of motor parameter and load torque variations. Finally, it is clearly confirmed that the proposed control method can accurately control the speed of a PMSM.

• Transactions on Control, Automation and Systems Engineering
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• v.1 no.2
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• pp.99-105
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• 1999

This paper presents a fuzzy H$\infty$ filtering problem for a class of uncertain nonlinear systems with time-varying delayed states and unknown inital state on the basis of Takagi-Sugeno(T-S) fuzzy model. The nonlinear systems are represented by T-S fuzzy models, and the fuzzy control systems utilize the concept of the so-called parallel distributed compensation. Using a single quadraic Lyapunov function, the stability and L2 gain performance from the noise signals to the estimation error are discussed. Sufficient conditions for the existence of fuzzy H$\infty$ filters are given in terms of linear matrix inequalities (LMIs). The filtering gains can also be directly obtained from the solutions of LMIs.

• Proceedings of the KIEE Conference
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• 2004.07d
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• pp.2215-2217
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• 2004

In this paper, a systematic design approach based on the parallel distributed compensation technique is proposed for chaotifying a general continuous-time Takagi-Sugeno (TS) fuzzy system. The fuzzy parallel distributed compensation controller (FPDCC) is composed of the feedback gain and time-delay feedback. The verification of chaos in the controlled continuous-time TS fuzzy system is done by the following procedures. First, we establish an asymptotically approximate relationship between a time-delay continuous-time TS fuzzy system and a discrete-time TS fuzzy system. Then, Marotto theorem is applied. Therefore, the generated chaos is in the sense of Li and Yorke. The boundedness in the controlled continuous-time TS fuzzy system is also proven via its associated discrete-time TS fuzzy system.