• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1203-1206
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• 1993

Fuzzy techniques are applied to the positioning of an elastic beam. The advantage is that the system model is not needed. A simple fuzzy friction compensator is also used. The final position is achieved within 3/2 the period of the fundamental mode. A fuzzy set of rules is applied for large-angle positioning, with adaptations that reduce the effects of shock. In this case, the final position is achieved within two fundamental periods. There is typically some final error attributed to the dry friction.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.1
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• pp.33-38
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• 2002

In this paper, a Two-Degree-of-Freedom-Controller(TDFC) for DC motors based on inverse dynamics and the fuzzy technique is presented. The proposed controller includes the inverse dynamic model of a DC motor system, a prefilter and a fuzzy compensator. The model of the system is characterized by a nonlinear equation with coulomb friction. The prefilter eliminates high frequency effects occurring when the inverse dynamic model is implemented. The fuzzy compensator is designed for tracking the change of the reference input and simultaneously regulating the error between the reference input and the system output which can be caused by disturbances. The optimal parameters of both the model and the compensator are identified by a real-coded genetic algorithm. An experimental work on a DC motor system is carried out to verify the performance of the proposed controller.

• Journal of the Institute of Convergence Signal Processing
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• v.6 no.3
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• pp.157-162
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• 2005

This paper presents an adaptive compensator using the fuzzy systems for robot manipulator with unknown frictions. In general, frictions are neglected or dynamic frictions are only considered in robot control theories. The proposed control method considers viscous frictions as well as dynamic frictions. Using the property that the frictions of joints are decoupled, SISO-fuzzy systems are utilized to approximate each friction. The stability of overall control system is proven and the adaptive laws are derived based on Lyapunov stability theorey. To verify the validity of the proposed control strategy, the results of computer simulations are shown for 2-link robot manipulator. The ability of approximating of the fuzzy system is also shown.

• Journal of the Institute of Convergence Signal Processing
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• v.3 no.1
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• pp.54-60
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• 2002

In this paper, we proposed a hybrid neural network-fuzzy controller which compensate a output of neural network controller. Even if learn by neural network controller, it can occur an bad results from disturbance or load variations. So in order to adjust above case, we used the fuzzy compensator to get an expected results. And the weight of main neural network can be changed with the result of loaming a inverse model neural network of Plant, so a expected dynamic characteristics of plant can be got. As the results of simulation through the second order plant, we confirmed that the proposed speed controller get a good response compare with a neural network controller. We implemented the controller using the DSP processor and applied in a hydraulic servo system. And then we observed an experimental results.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.1
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• pp.26-34
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• 2015

In this paper, a fuzzy compensated PID control system is proposed for formation control of mobile robots. The control system consists of a kinematic controller based on the leader-follower approach and a dynamic controller to handle dynamics effects of mobile robots. To maintain the desired formation of mobile robots, the dynamic controller is equipped with a PID controller; however, the PID controller has poor performance in nonlinear and changing environments. In order to improve these problem, we applied the additional fuzzy compensator. Finally, the proposed control system has been evaluated through computer simulation to demonstrate the improved results.

• International Journal of Control, Automation, and Systems
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• v.5 no.4
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• pp.364-371
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• 2007

In this paper, we describe the synthesis of robust and non-fragile $H^{\infty}$ state feedback controllers for linear systems with affine parameter uncertainties, as well as a static state feedback controller with poly topic uncertainty. The sufficient condition of controller existence, the design method of robust and non-fragile $H^{\infty}$ static state feedback controller with fuzzy compensator, and the region of controllers that satisfies non-fragility are presented. We show that the resulting controller guarantees the asymptotic stability and disturbance attenuation of the closed loop system in spite of controller gain variations within a resulted polytopic region.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2002.04a
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• pp.463-468
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• 2002

In this paper, we proposed a recurrent time delayed neural network controller which compensate a output of neural network controller. Even if learn by neural network controller, it can occur an bad results from disturbance or load variations. So in order to adjust above case, we used the fuzzy compensator to get an expected results. And the weight of main neural network can be changed with the result of learning a inverse model neural network of plant, so a expected dynamic characteristics of plant can be got. As the results of simulation through the second order plant, we confirmed that the proposed recurrent time delayed neural network controller get a good response compare with a time delayed neural network controller.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.402-403
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• 2011

This paper presents an active and reactive power compensator for the wind power system with multi-polar synchronous generator. The proposed compensator is composed of a charge/discharge PWM converter and battery. The output power of a wind power system changes irregularly according to the variation of wind speed. The developed system is able to continuously compensate the active and reactive power. The operational feasibility of the proposed model was verified by simulations with PSCAD/EMTDC.

• Proceedings of the KIEE Conference
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• 2007.04c
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• pp.180-182
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• 2007

An interior permanent magnet synchronous motors (IPMSM) are receiving increased attention for many industrial applications because of its high torque to inertia ratio, superior power density, and high efficiency. This paper presents algorithm for speed sensorless control based on an adaptive binary observer adding the fuzzy gain compensator. Effectiveness of algorithm is confirmed by the experiments.

• Proceedings of the KIEE Conference
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• 2005.07b
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• pp.1616-1618
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• 2005

In this paper, A fuzzy logic based turn-off angle compensator for torque ripple reduction in a linear switched reluctance motor is proposed. The turn-off angle, as a complex function of motor speed and current, is automatically changed for a wide speed range to reduce torque ripple. Simulation results are presented that show ripple reduction when the him-off angle compensator is used.