• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.769-772
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• 1995

This paper presents fuzzy control rules of automatically driving a car. Fuzzy control rules proposed are designed by investigating human experts' experiences and composed of three groups whose functions are different. According to computer simulations which let a model car pass through a curve of S type, we showed validity of fuzzy control rules suggested.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.275-278
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• 1996

The application of genetic algorithms to fuzzy rule generation holds a great deal of promise in overcoming difficult problems in fuzzy systems design. There are some aspects to be considered when genetic algorithms are used for generating fuzzy rules. In this paper, we will present an aspect about the control surface constructed by the resultant rules. In the extensive simulations, an important observation that the rules searched by genetic algorithms are randomly scattered is made and a solution to this problem is provided by including a smoothness cost in the objective function. We apply the fuzzy rules generated by genetic algorithms to the fuzzy truck backer-upper control system and compare them with the rules made by an expert.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2377-2379
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• 2000

Design of Fuzzy cotroller consists of intuition of human expert, and any other information about how to control system, they translated into a set of rules. If the rules adequately control the system, the design work is done well. If the rules are inadequate, the designer must modify the rules. Through this procedure, the system can be controlled. In this paper, we designed simply a fuzzy controller based on human knowledge, but it has errors showing some vibrations. So we updated the optimal parameters of fuzzy controller using Recursive least square algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.579-583
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• 1991

A fuzzy logic controller with rule modification capability is proposed to overcome the difficulty of obtaining control rules from the human operators. This new SOC algorithm modifies control rules by a fuzzy inference machine utilizing data base. Computer simulation results show good performances on both a linear system and a nonlinear system.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.7
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• pp.808-813
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• 1999

A fuzzy learning method to control an unstable and multivariable system is presented in this paper, Because the multivariable system has generally a coupling effect between the inputs and outputs, it is difficult to find its modeling equation or parameters. If the system is unstable, initial condition rules are needed to make it stable because learning is nearly impossible. Therefore, this learning method uses the initial rules and introduces a cost function composed of the actual error and error-rate of each output without the modeling equation. To minimize the cost function, we experimentally got the Jacobian matrix in the operating point of the system. From the Jacobian matrix, we can find the direction of the convergence in the learning, and the optimal control rules are finally acquired when the fuzzy rules are updated by changing the portion of the errors and error rates.

• Journal of the Korean Institute of Intelligent Systems
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• v.6 no.2
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• pp.43-51
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• 1996

Fuzzy rules in fuzzy logic control play a major role in deciding the control dynamics of a fuzzy logic controller. Thus, control performance is mainly determined by the quality of fuzzy rules. This paper introduces an optimization method for fuzzy rules using GAS with adaptive probabilies of crossover and mutation. Also we design two fitness measures to satisfy control objectives by partitioning the response of a plant into two parts. An initial population is generated by an automatic fuzzy rule generation method instead of random selection for fast a.pproaching to the final solution. We employed a nonlinear plant to simulate our method. It is shown through simulation that our method is reasonable and can be useful for optimizing fuzzy rules.

• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.539-541
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• 1997

Fuzzy controller design consists of intuition, and any other information about how to control system, into a set of rules. These rules can then be applied to the system. If the rules adequately control the system, the design work is done well. If the rules are inadequate, the designer must modify the rules. Through this procedure, the system can be controlled. In this paper, we design fuzzy controller composed of two parts, one is balancing controller, the other is angle controller.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.2
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• pp.155-160
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• 2006

In the paper a robust indirect adaptive fuzzy controller is proposed for balancing and position control of the inverted pendulum system. Because balancing control rules of the pendulum and position control rules of the cart can be opposite, it is difficult to design an adaptive fuzzy controller that satisfy both objectives. To stabilize the pendulum at a specified position, the proposed fuzzy controller consists of a robust indirect adaptive fuzzy controller for balancing and a supervisory fuzzy controller which emulates heuristic control strategy and arbitrate two control objectives. It is proved that the signals in the overall system are bounded. Simulation results are given to verify the proposed adaptive fuzzy control method.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.1
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• pp.93-99
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• 2003

One of the methods to simplify the design process for a fuzzy logic controller (FLC) is to reduce the number of variables representing the rule antecedent. This in turn decreases the number of control rules, membership functions, and scaling factors. For this purpose, we designed a single-input FLC that uses a sole fuzzy input variable. However, it is still deficient in the capability of adapting some varying operating conditions although it provides a simple method for the design of FLC's. We here design two simple-structured adaptive fuzzy logic controllers (SAFLC's) using the concept of the single-input FLC. Linguistic fuzzy control rules are directly incorporated into the controller by a fuzzy basis function. Thus some parameters of the membership functions characterizing the linguistic terms of the fuzzy control rules can be adjusted by an adaptive law. In our controllers, center values of fuzzy sets are directly adjusted by an adaptive law. Two SAFLC's are designed. One of them uses a Hurwitz error dynamics and the other a switching function of the sliding mode control (SMC). We also prove that 1） their closed-loop systems are globally stable in the sense that all signals involved are bounded and 2） their tracking errors converge to zero asymptotically. We perform computer simulations using a nonlinear plant.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.1
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• pp.91-96
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• 2003

The forces and moments at the aircraft c.g. have components due to aerodynamic effects and to engine thrust. For the flight stability and autopilot systems we present a attitude control method using an intelligent control algorithm Which is based on the control rules from experts knowledge concerning the motion equations and other experiences. Then a robust fuzzy controller is developed to control the flight attitude. The controller can deal with multiple inputs and outputs. We have made an aircraft model and the orientation sensor for experimental flights. The control rules based on the flight expert s experience and knowledge can be programmed by fuzzy rules, and determined control rules by experimental flight. We can be stable attitude control by fuzzy controller.