• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.5
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• pp.307-314
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• 2005

In this paper, we propose a predictive system for the avoidance of the moving obstacle. In the dynamic environment, robots should travel to the target point without collision with the moving obstacle. For this, we need the prediction of the position and velocity of the moving obstacle. So, we use the Kalman filer algorithm for the prediction. And for the application of the Kalman filter algorithm about the real time travel, we obtain the position of the obstacle which has the future time using Fuzzy system. Through the computer simulation studies, we show the effectiveness of the proposed navigational algorithm for autonomous mobile robots.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.6
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• pp.100-108
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• 1993

In this paper, noticing the point of human's ability which appropriately cope with vague conditions, we design fuzzy traffic signal light controller similar to human's distinction ability and decide the optimum cycle most suited to any traffic junction using fuzzy identification algorithm. In this study, for the control output decision process we design fuzzy controller better than electronic vehicle actuated controller in performance. We propose the cycle decision method which is not limited by the variance of traffic junction vehicle number through overcoming the limit of Webster's method which is adopted by the fixed cycle controller. Simulated experimental results show that fuzzy controller and fuzzy identification algorithm are better than the existing electronic vehicle actuated controller and fixed cycle controller in delay time per vehicle.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.228-231
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• 2000

A hybrid fuzzy controller by means of the genetic algorithms is presented. The control input for the system in the HFC is a convex combination of the FLC's output in transient state and PlD's output in steady state by a fuzzy variable. The HFC combined a PID controller with a fuzzy controller concurrently produces the better output performance than any other controller. A auto-tuning algorithms is presented to automatically improve the performance of hybrid fuzzy controller using genetic algorithms. The algorithms estimates automatical Iy the optimal values of scaling factors, PID parameters and membership function parameters of fuzzy control rules. Especially, in order to auto-tune scaling factors and PID parameters of HFC using GA three kinds of estimation modes are effectively utilized. The HFCs are applied to the second process with time-delay. Computer simulations are conducted at step input and the performances of systems are evaluated and also discussed in ITAE(Integral of the Time multiplied by the Absolute value of Error ) and other ways.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.1
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• pp.150-155
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• 2011

In an active noise control systems, an IIR filter may cause a problem in stability beacause of its poles. For IIR filter, its poles goes sometimes out of a unit circle in a z-plane in the transition state, where the adaptive algorithm converges to the optimum value, which causes the system to diverge. Fuzzy LMS algorithm has a better convergence property than conventional LMS algorithms, but is not applicable to IIR filter because of the reasons. Stabilized adaptive algorithm could be improves stability by moving the pole of IIR filer toward the origin forcibly in the transient state, and by introducing forgetting factor to maintain the optimum convergence when it reaches to the steady state. In this paper, We proposed stabilized adaptive fuzzy LMS algorithms with IIR filter structures, for single channel active noise control with ill conditioned signal case. Computer simulations were performed to show the effectiveness of a proposed algorithm.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.43 no.4
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• pp.627-637
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• 1994

In this study, a predictive fuzzy control algorithm to supervise the elevator system with plural cars is developed and its performance is evaluated. The proposed algorithm is based on fuzzy in-ference system to cope with multiple control objects and uncertainty of system state. The control objects are represented as linguistic predictive fuzzy rules and simplified reasoning method is utilized as a fuzzy inference method. Real-time simulation is performed with respect o all possible modes of control, and the resultant controls ard predicted. The predicted rusults are then utilized as the control in-puts of the fuzzy rules. The feasibility of the proposed control algorithm is evaluated by graphic simulator on computer. Finallu, the results of graphic simulation is compared with those of a conventional group control algorighm.

• Proceedings of the KIEE Conference
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• 1998.07g
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• pp.2367-2369
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• 1998

This paper presents simulation experiment results of the fuzzy controller using genetic algorithms for robot manipulator. The fuzzy controller consists of 9 quantized levels and 25 fuzzy rules. In the simulations, the population size of each generation is set to be 100. The maximum number of generations is 200. The simulation experiment results show the effectiveness of the proposed the fuzzy controller using genetic algorithms.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2458-2460
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• 2000

The goal of crane control system is transporting heavy objects to a target position as fast as possible without rope oscillation. This paper presents a GA-based fuzzy logic controller for crane system. GA is going to decide membership functions, instead of an expert. In this paper, The centers and widths of the membership function of the fuzzy sets defined over the input space, the orders and parameters of subsystems in the consequence parts are adjusted by a genetic algorithm. The effectiveness of the proposed method is verified by simulation.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.3187-3188
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• 2000

Based on fuzzy control algorithm this paper constructed fuzzy controller for automated vehicles. For passenger's convenience especially comfortability controller need to reduce the frequency of input variable's changing. So we established membership functions for comfortability as well as speed following. It made possible to control comfortability directly. To demonstration the efficiency of fuzzy controller, we carried out simulation with a automobile's transfer function. Also we compared the difference of input variable. By comparing two controller's response, we can confirm the merit of fuzzy controller about comfortability. Fuzzy controller can reduce input changing frequency.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2837-2839
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• 2000

We design a stable controller for a mobile robot with variable gains and reference velocity in order to apply the proper gains and reference velocity, which are generated with fuzzy logic in on-line. The stability is guranteed by the Lyapunov theory. The fuzzy logic rules is found in off-line with GA strategy which drives each object function to be the least. The proposed controller is applied smooth path tracking due to the local path planing. Simulation results show robust performances under a different initial conditions.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.999-1001
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• 1995

In this study, the algorithm of optimal parameter inference is proposed. At this inferring method, we tried to acquire the follow-up to reference pattern through comparing the plant output pattern with random reference pattern. As an inference method, the fuzzy theory was applied and the proposed algorithm was proved by computer simulation.