• 대한전자공학회:학술대회논문집
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• 대한전자공학회 1999년도 추계종합학술대회 논문집
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• pp.1149-1152
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• 1999

In this paper we analyzed each region of specific points and ｅ-Δｅphase plane in order to make fuzzy rule base. After we composed the fuzzy control rules which can decrease rise time, delay time, maximum overshoot than basic fuzzy control rules. The composed method are converged more rapidly than single rule base in convergence region. Proposed method is alternately use at specific points of ｅ-Δｅphase plane with two fuzzy control rules, that is one control rule occruing the steady state error used in transient region and another fuzzy control rule use to decrease the steady state error and rapidly converge at the convergence region. Two fuzzy control rules in the ｅ-Δｅ phase plane decide the change time according to response characteristics of plants. As the results of simulation through the second order plant and the delay time plan, Proposed dual fuzzy control rules get the good response compare with the basic fuzzy control rule.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1991년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 22-24 Oct. 1991
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• pp.1950-1955
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• 1991

A fuzzy dynamic learning controller is proposed and applied to control of time delayed, non-linear and unstable chemical processes. The proposed fuzzy dynamic learning controller can self-adjust its fuzzy control rules using the external dynamic information from the process during on-line control and it can create th,, new fuzzy control rules autonomously using its learning capability from past control trends. The proposed controller shows better performance than the conventional fuzzy logic controller and the fuzzy self organizing controller.

• 융합신호처리학회논문지
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• 제8권3호
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• pp.222-226
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• 2007

본 연구에서는 응답의 과도영역에서 특징점의 각 영역과 $e-{\Delta}e$ 위상평면을 이용하여 이중 퍼지룰을 구성하였다. 구성된 퍼지 제어룰은 기본 퍼지제어 룰에 비해 상승시간, 지연시간, 최대오브슈트 등을 줄일 수 있었으며 퍼지 룰의 제안방법은 $e-{\Delta}e$ 위상평면에서 하나의 퍼지룰은 과도영역에서 정상상태오차를 일으키고 다른 퍼지 제어 룰은 정상상태오차를 감소시키고 수렴영역에서 빠르게 수렴하도록 교대로 사용한다. 또한 $e-{\Delta}e$ 위상평면에서의 두 개의 퍼지 룰은 플랜트의 응답 특성에 따라 교체시기를 결정한다. DSP 프로세서를 사용하는 유압서보 모터 제어시스템을 통한 실험의 결과로서 제안된 이중 퍼지 제어 룰은 기본 퍼지 제어 룰에 비해 빠른 상승시간, 정정시간등 양호한 응답을 얻을 수 있음을 확인하였다.

• 대한기계학회논문집A
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• 제20권2호
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• pp.399-407
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• 1996

A fuzzy learning algorithm to get the optimal fuzzy rules is presented in this paper. The algorithm introduces a reference model to generate a desired output and a performance index funtion instead of the performance index table. The performance index funtion is a cost function based on the error and error-rate between the reference and plant output. The cost function is minimized by a gradient method and the control input is also updated. In this case, the control rules which generate the desired response can be obtained by changing the portion of the error-rate in the cost funtion. In SISO(Single-Input Single- Output)plant, only by the learning delay, it is possible to experss the plant model and to get the desired control rules. In the long run, this algorithm gives us the good control rules with a minimal amount of prior informaiton about the environment.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1997년도 춘계학술대회 학술발표 논문집
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• pp.145-150
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• 1997

In this paper is studied a method of fuzzy logic control based on possibly inconsistent if-then rules representing uncertain knowledge or imprecise data. In most cases of practical applications adopting fuzzy if-then rule bases, inconsistent rules have been considered as ill-defined rules and, thus, not allowed to be in the same rule base. Note, however, that, in representing uncertain knowledge by using fuzzy if-then rules, the knowledge sometimes can not be represented in literally consistent if-then rules. In this regard, when it is hard to obtain consistent rule base, we propose the weighted rule base fuzzy logic control depending on output performance using neural network and we will derive the weight update algorithm. Computer simulations show the proposed method has good performance to deal with the inconsistent rule base fuzzy logic control. And we discuss the real application problems.

• Journal of information and communication convergence engineering
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• 제6권2호
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• pp.222-227
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• 2008

The important problem in designing a Fuzzy Logic Controller(FLC) is generation of fuzzy control rules and it is usually the case that they are given by human experts of the problem domain. However, it is difficult to find an well-trained expert to any given problem. In this paper, I describes an application of genetic algorithm, a well-known global search algorithm to automatic generation of fuzzy control rules for FLC design. Fuzzy rules are automatically generated by evolving initially given fuzzy rules and membership functions associated fuzzy linguistic terms. Using genetic algorithm efficient fuzzy rules can be generated without any prior knowledge about the domain problem. In addition expert knowledge can be easily incorporated into rule generation for performance enhancement. We experimented genetic algorithm with a non-trivial vehicle controling problem. Our experimental results showed that genetic algorithm is efficient for designing any complex control system and the resulting system is robust.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.40.6-40
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• 2001

Fuzzy controls are widely used in industrial fields using experts knowledge base for its high degree of performance. Genetic Algorithm(GA) is one of the numerical method that has an advantage of optimization. In this paper, we present an acquisition method of fuzzy rules using genetic algorithm. Knowledge of the system is the key to generating the control rules. As these rules, a system can be more stable and it reaches the control goal the faster. To get the optimal fuzzy control rules and the membership functions, we use the GA instead of the experts knowledge base. Information of the system is coded the chromosome with suitable phenotype. Then, it is operated by genetic operator, and evaluated by evaluation function. Passing by the decoding process with the fittest chromosome, the genetic algorithm can tune the fuzzy rules and the membership functions automatically ...

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1992년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 19-21 Oct. 1992
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• pp.473-478
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• 1992

This paper aims to investigate the navigation control of a mobile robot in a confined environment. Steering angle becomes control variable which is computed from the fuzzy control rules. The identification method proposed in this paper presents the fuzzy control rules obtained through modelling of. the driving actions of human operator. The feasibility of the proposed method is evaluated through the application of the identified fuzzy controls rules to the navigation control of a mobile robot which follows the center of a corridor.

• 한국산업융합학회 논문집
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• 제9권3호
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• pp.173-178
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• 2006

For the design of multivariable fuzzy control systems decomposition of control rules is a efficent inference method since it alleviates the complexity of the problem. In some systems, however, inference error of the Gupta's decomposition method is inevitable because of its approximate nature. In this paper we define indices of applicability which decides whether the decomposition method can be applied to a multivariable fuzzy system or not.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.96.2-96
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• 2001

This paper presents a control method for the experimental mobile vehicle. By merging the advantages of neural network, adaptive and fuzzy control, neural network-based adaptive fuzzy control is proposed. It can deal with a large amount of training data by neural network, from these data producing more accurate fuzzy rules by adaptive control, and then controlling the object by fuzzy control. This is not the simple combination of the three methods, but merging them into one control system Experiments and some future considerations are given.