• Proceedings of the IEEK Conference
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• 1998.06a
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• pp.897-900
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• 1998

Through fuzzy logic controller is very useful to many areas, it is difficult to build up the rule-base by experience and trial-error. So, effective self-tuning fuzzy controller for the position control of ball and beam is designed. In this paper, we developed the neural network control system with fuzzy-neuron which conducts the adjustment process for the parameters to satisfy have nonlinear property of the ball and beam system. The proposed algorithm is based on a fuzzy logic control system using a neural network learinign algorithm which is a back-propagation algorithm. This system learn membership functions with input variables. The purpose of the design is to control the position of the ball along the track by manipulating the angualr position of the serve. As a result, it is concluded that the neural network control system with fuzzy-neuron is more effective than the conventional fuzzy system.

• Proceedings of the Korean Nuclear Society Conference
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• 1996.11a
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• pp.85-90
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• 1996

In this paper, the self-organizing fuzzy logic controller is developed for water level control of steam generator. In comparison with conventional fuzzy logic controllers, this controller performs control task with no control rules at initial and creates control rules as control behavior goes on, and also modifies its control structure when uncertain disturbance is suspected. Selected parameters in the fuzzy logic controller are updated on-line by the gradient descent loaming algorithm based on the performance cost function. This control algorithm is applied to water level control of steam generator model developed by Lee, et al. The computer simulation results confirm good performance of this control algorithm in all power ranges. This control algorithm can be expected to be used for automatic control of feedwater control system in the nuclear power plant with digital instrumentation and control systems.

• Journal of Korean Institute of Industrial Engineers
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• v.28 no.1
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• pp.76-86
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• 2002

In this paper, we suggest a hybrid genetic algorithm reinforced by a fuzzy logic controller (flc-HGA) to overcome weaknesses of conventional genetic algorithms: the problem of parameter fine-tuning, the lack of local search ability, and the convergence speed in searching process. In the proposed flc-HGA, a fuzzy logic controller is used to adaptively regulate the fine-tuning structure of genetic algorithm (GA) parameters and a local search technique is applied to find a better solution in GA loop. In numerical examples, we apply the proposed algorithm to a simple test problem and two complex combinatorial optimization problems. Experiment results show that the proposed algorithm outperforms conventional GAs and heuristics.

• Proceedings of the KIEE Conference
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• 2005.07d
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• pp.2714-2716
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• 2005

In this paper, we designed the adaptive fuzzy logic controller for crane system using neural network and real-coding genetic algorithm. The proposed algorithm show a good performance on convergence velocity and diversity of population among evolutionary computations. The weights of neural network is adaptively changed to tune the input/output gain of fuzzy logic controller. And the genetic algorithm was used to leam the feedforward neural network. As a result of computer simulation, the proposed adaptive fuzzy logic controller is superior to conventional controllers in moving and modifying the destination point.

• Transactions on Control, Automation and Systems Engineering
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• v.1 no.2
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• pp.147-152
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• 1999

It is an interesting area in the field of artifical intelligence to find an analytic model of cooperative structure for multiagent system accomplishing a given task. Usually it is difficult to design controllers for multi-agent systems without a comprehensive knowledge about the system. One of the way to overcome this limitation is to implement an evolutionary approach to design the controllers. This paper introduces the use of a genetic algorithm to discover a fuzzy logic controller with rules that govern emergent agents solving a pursuit problem in a continuous world. Simulation results indicate that, given the complexity of the problem, an evolutionary approach to find the fuzzy logic controller seems to be promising.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.11a
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• pp.950-957
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• 2003

This paper deals with adaptive fuzzy logic controller design to achieve proper dynamic response of a composite thin-walled beam with a tip mass. In order to check the effectiveness of this controller, three different types of control logic are selected and applied. The adaptive control capabilities provided by a system of piezoactuators bonded or embedded into the structure are also implemented in the system. Results show that the fuzzy logic controller is more effective than the proportional or velocity feedback controller for the vibration control of composit thin-walled beam with a tip mass.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.05a
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• pp.113-116
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• 2001

This paper analyzes the evolutionary process of a fuzzy logic controller using evolutionary activity. An evolutionary algorithm is commonly used to find solutions for given problems. However, little has been done on the analysis of the evolutionary pathways to the optimal solutions. This paper uses a genetic algorithm to construct a fuzzy logic controller for a mobile robot and applies evolutionary activity to measure the adaptability quantitatively. Evolutionary activity can be defined as the rate at which useful genetic innovations are absorbed in the population. By measuring the evolutionary activities, we will show quantitatively that the optimal fuzzy logic controller is not from other genetic phenomena like chance or necessity, but from the adaptability to a given encironment.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.507-512
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• 1998

It is an interesting area in the field of artificial intelligence to and an analytic model of cooperative structure for multi-agent system accomplishing a given task. Usually it is difficult to design controllers for multi-agent systems without a comprehensive knowledge about the system. One of the way to overcome this limitation is to implement an evolutionary approach to design the controllers. This paper introduces the use of a genetic algorithm to discover a fuzzy logic controller with rules that govern emergent co-operative behavior: A modified genetic algorithm was applied to automating the discovery of a fuzzy logic controller jot multi-agents playing a pursuit game. Simulation results indicate that, given the complexity of the problem, an evolutionary approach to and the fuzzy logic controller seems to be promising.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.2
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• pp.215-219
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• 1998

This paper presents a practical application of fuzzy logic control to temperature control of glass melting furnace. Due to the characteristics of glass melting furnace, a hybrid algorithm of conventional PI controller and fuzzy logic controller is proposed and discussed. Practical implementation results of the production furnace showed the effectiveness of the proposed control algorithm.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.244-249
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• 1998

Abstract-A speed controller of a induction motor using Microcontroller and Fuzzy logic is presented in the paper. Generally fuzzy logic controller is known as a controller which 춤 be coped with a non-linear and a complex system. A fuzzy logic is used for robust and fast speed control and space vector modulation method is used for PWM wave generation in this proposed system. The results of experiment show excellence of the proposed system and that the proposed system is appropriate to control the speed of a induction motor for industrial application