• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.11a
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• pp.77-80
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• 1997

In this paper, we propose a new fitnesness function of GA for slowly time-varying plant. Previous Takgi-Sugeno model based controller is used as basic control scheme and Controller parameters are tuned by GA with the proposed fitness function includes the information of model parameter variation and has better performance robustness than the previous ones. We illustrate the effectiveness of the proposed fitness function by simple simulation example.

• Proceedings of the KSME Conference
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• 2000.11a
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• pp.570-575
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• 2000

This paper presents a delayed time path planning method of the Autonomous Mobile Robot using fuzzy logic controller for avoidance of obstacles in unknown environment. It is the objective of this paper to develop fuzzy control algorithms using delayed time techniques to deal with moving obstacles randomly. This control method gives the benefit of the collision free movement in real time and optimal path to the pre-settled goal. The computer simulations are demonstrated the effective of the suggested control method in obstacle avoidance.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.2 no.3
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• pp.242-247
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• 2002

According to increase of the factory-automation(FA) in the field of production, the importance of the autonomous guided vehicle's(AGV) role has also increased. This paper is about an active and effective controller which can flexibly prepare for changeable circumstances. For this study, research about an behavior-based system evolving by itself is also being considered. In this Paper, we constructed an active and effective AGV fuzzy controller to be able to carry out self-organization. To construct it, we tuned suboptimally membership function using a genetic algorithm(GA) and improved the control efficiency by self-correction and the generation of control rules.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.5 s.98
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• pp.104-111
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• 1999

A semi-active suspension system for a vehicle using an Electrorheological Fluid damper has been studied. Apparent viscosity of ERF(Electrorheological Fluid) can be changed rapidly by applying electric field. The damping force of ER damper can be selectively controlled by employing electric field to the ER fluid domain. This paper deals with a two-degree-of-freedom suspension using the ER damper for a quarter car model. An intelligent control method using fuzzy control with genetic algorithm has been employed to control the damping force of the ER damper. The GA designs the optimal structure and performance of Fuzzy Net Controller having hybrid structure. The designed fuzzy net controller has been compared with the skyhook type controller for a quarter car model. The computer simulation results show that the semi-active suspension with ER damper has a good performance in the sense of ride quality with less vibration for ground vehicle.

• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.728-731
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• 1997

This paper proposes the systematic auto-tuning method for fuzzy controller using genetic algorithm(GA). In general, the design of fuzzy logic controller has difficulties in the acquisition of expert's knowledge and relies to a great extent on heuristic knowledge which, in many cases, cannot be objectively justified. So, the performance of the controller can be degraded in the case of plant parameter variations or unpredictable incident which the designer may have ignored. Proposed genetic algorithm searches the optimal rule structure, parameters of membership functions and scaling factors simultaneously and automatically by a new genetic coding format. Inverted pendrum system is provided to show the advantages of the proposed method.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1949-1954
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• 2005

One of control methods for underactuated manipulators is known as a switching control which selects a partially-stable controller using a prespecified switching rule. A switching computed torque control with a fuzzy energy region method was proposed. In this approach, some partly stable controllers are designed by the computed torque method, and a switching rule is based on fuzzy energy regions. Design parameters related to boundary curves of fuzzy energy regions are optimized offline by a genetic algorithm (GA). In this paper, we discuss on parameters obtained by GA. The effectiveness of the switching fuzzy energy method is demonstrated with some simulations.

• Proceedings of the KIEE Conference
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• summer
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• pp.292-294
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• 2004

In this paper, we design the GA-fuzzy precompensator of a Power System Stabilizer for Thyristor Controlled Series Capacitor(TCSC-PSS) for enhancement of power system stability. Here a fuzzy precompensator is designed as a fuzzy logic-based precompensation approach for TCSC-PSS. This scheme is easily implemented simply by adding a fuzzy precompensator to an existing TCSC-PSS. And we optimize the fuzzy precompensator with a genetic algorithm for complements the demerit such as the difficulty of the component selection of fuzzy controller, namely, scaling factor, membership Auction and control rules. Simulation results show that the proposed control technique is superior to a conventional PSS in dynamic responses over the wide range of operating conditions and is convinced robustness and reliableness in view of structure.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.9
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• pp.95-103
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• 1995

Recently the trends toward reduction in size of industrial products have increased the application of micro drilling. But micro drilling has still much difficulty so that the needs for active control which give adaptation to controller are expanding. In this paper initial cutting condition was determined for some sorkpieces by experiment and GA-based Fuzzy controller was devised by genetic algorithms and fuzzy inference. The fuzzy inference has been applied to the various prob- lems. However the determination of the membership function is one of the difficult problem. So we introduce a genetic algorithms and propose a self-tuning method of fuzzy membership function. Based on this intelligent control, automation of micro drilling was carried out like the cutting process of skilled machinist.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.1
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• pp.114-120
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• 2017

The work present in this paper deals with a navigation problem for multiple mobile robots in unknown indoor environments. The environments are completely unknown to the robots; thus, proximity sensors installed on the robots' bodies must be used to detect information about the surroundings. The environments simulated in this work are dynamic ones which contain not only static but also moving obstacles. In order to guide the robot to move along a collision-free path and reach the goal, this paper presented a navigation method based on fuzzy approach. Then genetic algorithms were applied to optimize the membership functions and rules of the fuzzy controller. The simulation results verified that the proposed method effectively addresses the mobile robot navigation problem.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.67 no.3
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• pp.143-148
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• 2018

In this paper, the optimal fuzzy logic controller(FLC) for a hybrid renewable energy system(HRES) is proposed. Generally, hybrid renewable energy systems can consist of wind power, solar power, fuel cells and storage devices. The proposed FLC can effectively control the entire HRES by determining the output power of the fuel cell or the absorption power of the electrolyzer. In general, fuzzy logic controllers can be optimized by classical optimization algorithms such as genetic algorithms(GA) or particle swarm optimization(PSO). However, these FLC have a disadvantage in that their performance varies greatly depending on the control parameters of the optimization algorithms. Therefore, we propose a method to optimize the fuzzy logic controller using the teaching-learning based optimization(TLBO) algorithm which does not have the control parameters of the algorithm. The TLBO algorithm is an optimization algorithm that mimics the knowledge transfer mechanism in a class. To verify the performance of the proposed algorithm, we modeled the hybrid system using Matlab Tool and compare and analyze the performance with other classical optimization algorithms. The simulation results show that the proposed method shows better performance than the other methods.