• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.140-140
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• 2000

Traditional genetic algorithms, though robust, are generally not the most successful optimization algorithm on any particular domain. Hybridizing a genetic algorithm with other algorithms can produce better performance than both the genetic algorithm and the other algorithms. In this paper, we use random process and simulated annealing instead of mutation operator in order to get well tuned fuzzy rules. The key of this approach is to adjust both the width and the center of membership functions so that the tuned rule-based fuzzy controller can generate the desired performance. The effectiveness of the proposed algorithm is verified by computer simulation.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.2
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• pp.131-137
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• 2001

Traditional genetic algorithms, though robust, are generally not the most successful optimization algorithm on only particular domian. Hybridizing a genetic algorithm with other algorithms can produce better performance than both the genetic algorithm and the other algorithms. This paper describes the application of random signal-based learning to a genetic algorithm in order to get well tuned fuzzy rules. The key of tis approach is to adjust both the width and the center of membership functions so that the tuned rule-based fuzzy controller can generate the desired performance. The effectiveness of the proposed algorithm is verified by computer simulation.

• Transactions on Control, Automation and Systems Engineering
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• v.3 no.2
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• pp.117-123
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• 2001

To improve control performance of a non-linear system, many other reserches have used the sliding model control algorithm. The sliding mode controller is known to be robust against nonlinear and unmodeled dynamic terms. However, this algorithm raises the inherent chattering caused by excessive switching inputs around the sliding surface. Therefore, in order to solve the chattering problem and improve control performance, this study has developed the sliding mode controller with a perturbation estimator using the observer-based fuzzy adaptive network. The perturbation estimator based on the fuzzy adaptive network generates the control input of compensating unmodeled dynamics terms and disturbance. And the weighting parameters of the fuzzy adaptive network are updated on-line by adaptive law in order to force the estimation errors converge to zero. Therefore, the combination of sliding mode control and fuzzy adaptive network gives rise to the robust and intelligent routine. For evaluation control performance of the proposed approach, tracking control simulation is carried is carried out for the hydraulic motion simulator which is a 6-degree of freedom parallel manipulator.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.921-923
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• 1999

In adaptive fuzzy control, fuzzy systems are used to approximate the unknown plant nonlinearities. However, because of the approximating error introduced into the feedback loop, it is difficult to guarantee the stability of the adaptive control algorithm. This paper presents a robust control algorithm against the reconstruction error and uniform boundedness of the all signals is estabilished in the Lyapunov sense.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.2
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• pp.123-128
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• 2002

A fuzzy traffic controller with the control rules and the membership functions generated by using genetic algorithm is presented for crossroad management. Conventional fuzzy traffic controllers use control rules and membership functions generated by human operators. However, this approach does not guarantee the optimal solution to design fuzzy control system. Genetic algorithm is a good solution for an optimal problem requiring domain-specific knowledge that is often heuristic. In this paper, we use genetic algorithms to automatically determine the near optimal rules and their membership functions of fuzzy traffic controllers. The effectiveness of our method was shown through simulation of crossroad network.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.450-453
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• 1997

An active control for the swing of crane systems is very important for increasing the productivity. This article introduces the control for the position and the swing of a crane using the fuzzy learning method. Because the crane is a multi-variable system, learning is done to control both position and swing of the crane. Also the fuzzy control rules are separately acquired with the loading and unloading situation of the crane for more accurate control. The result of simulations shows that the crane is just controlled for a very large swing angle of 1 radian within nearly one cycle.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.49.5-49
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• 2001

The Proportional-Integral-Derivative control scheme is widely used in industries. This paper investigates an alternative control paradigm for controlling lightweight Cartesian robot arms. Fuzzy PI control is used and validated experimentally by comparing performance with a conventional PID control algorithm. The results show the effectiveness of the fuzzy PI control. The fuzzy control shows superior performance in transient response over the conventional one.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.1
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• pp.219-224
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• 2016

The vibration control performance of a semi-active damper connected to adjacent buildings subjected to seismic loads was investigated. The MR damper was used as a semi-active control device. A fuzzy logic control algorithm was used for effective control of the adjacent buildings connected to the MR damper. In the buildings control coupled with a MR damper, the response reduction of one building results in an increase in the response in another building. Because of these conflict characteristics, multi-objective optimization is required. Therefore, a fuzzy logic control algorithm for the control of a MR damper was optimized using a multi-objective genetic algorithm. Based on numerical analyses, the semi-active fuzzy control of MR damper for adjacent coupled buildings can provide good control performance.

• Journal of Advanced Marine Engineering and Technology
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• v.28 no.5
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• pp.793-800
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• 2004

The purpose of this study is to develope a servo system with faster and more accurate response. This paper describes a method of approximate reasoning for fuzzy control of servo system based on the decomposition of $\alpha$-level fuzzy sets. We propose that fuzzy logic algorithm is a body from fuzzy inference to defuzzificaion cases where the output variable u directly is generated PWM The effectiveness for robust and faster response of the fuzzy control scheme are verified for a variable parameter by comparison with a PID control and fuzzy control A position control of DC servo system with a fuzzy logic controller is demonstrated successfully.

• The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.9 no.6
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• pp.71-77
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• 1995

This paper deals with design method of learning fuzzy controller for control of an unknown nonlinear plant using the self-tuning algorithm of fuzzy inference rules. In this method the fuzzy identification model obtained that the joined identification model of nonlinear part and linear identification model of linear part by fuzzy inference systems. This fuzzy identification model ordered self-tuning by Decent method so as to be servile to nonlinear plant. A the end, designed learning fuzzy controller of fuzzy identification model have learning structure to model reference adaptive system. The simulation results show that th suggested identification and learning control schemes are practically feasible and effective.