• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.139-143
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• 2004

Lee et al have proposed a framework for the linguistic map-based navigational planning of a mobile robot on dynamic environment and provided simulation results applied it to the static environment[1], In this paper, we extends the navigational planning of a mobile robot into dynamic environment. There are two kinds of dynamic obstacles： (1) Time-obstacles that change condition of obstacles with time. (2) Space-obstacles that move their position with time. We propose an algorithm which a mobile robot identifies and avoids the two kinds of dynamic obstacles. The proposed algorithm consists of two stages： (1) The fuzzy logic-based perception stage which identifies the dynamic obstacles around a mobile robot by using sensory data and fuzzy rules, (2) The planning stage which plans the path to goal by avoiding the dynamic obstacles[2-6]. We provide computer simulation results for a mobile robot in order to show the validity of the proposed algorithm.

• Journal of the Korean Institute of Intelligent Systems
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• v.5 no.4
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• pp.87-100
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• 1995

In order to control systems which contain nonlinearities of uncertainties, control strategies must deal with the effects of them. Since most of control methods based on system mathematical models have been mainly developed focused on stability robustness against nonlinearities or uncertainties under the assumption that controlled systems are linear time invariant, they have certain amount of limitations to smartly improve the transient responses of systems disturbed by nonlinearities or uncertainties. In this paper, a nonlinear fuzzy PID control method is suggested which can stably improve the transient responses of systems disturbed by nonlinearities, as well as systems whose mathematical characteristics are not perfectly known. Although the derivation process is based on the design process similar to general fuzzy logic controller, resultant control law has analytical forms with time varying PID gains rather than linguistic forms, so that implementation using common-used versatile microprocessors cna be achieved easily and effectively in real-time control aspect.

• Journal of Advanced Marine Engineering and Technology
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• v.36 no.7
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• pp.927-934
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• 2012

This paper suggests a new fuzzy PID controller with variable parameters which improves the shortage of the fuzzy PID controller with fixed parameters suggested in [9]. The derivation procedure follows the general design procedure of the fuzzy logic controller, while the resultant control law is the form of the conventional PID controller. Therefore, the suggested controller has two advantages. One is that it has only four fuzzy linguistic rules and analytical form of control laws so that the real-time control system can be implemented based on low-price microprocessors. The other is that the PID control action can always be achieved with time-varying PID controller gains only by adjusting the input and output scalers at each sampling time.

• The Research Journal of the Costume Culture
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• v.2 no.2
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• pp.317-335
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• 1994

The purpose of this thesis is to analyze the relation of the mentalistics linguistics and costume, and to clarify the relationship between language as psychological expressions and costume to understand the phenomena of human costume behavior more deeply and comprehensively. As for the analysis of he costume phenomena, Noam Chomsky's psychological linguistic theory were applied to costume system. In this respect, particularly, by means of Chomsky's later theory(1965), the costume behavior were analyzed. The followings are the findings of the analysis : 1. The Syntactic Component: (※ See Full-Text) 2. The Semantic Component. The costme behavior is similar to a language system. Just as one morpheme or a phrase and phrases make different sentences, so various methods wearing costume make different sentences, so various methods wearing costume make different forms is costume. Language and costume have dictionary entry showing the meaning of vocabulary, and rules combining the individual meaning of the dictionary entry to complete the sentence.

• Journal of Industrial Technology
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• v.16
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• pp.231-237
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• 1996

In this paper, the nonlinear I/O characteristic of fuzzy logic controller is analyzed by using cell concept. Sources of the nonlinearity in a fuzzy logic controller include the fuzzification, the fuzzy reasoning and the defuzzification. A closed form expression for the defuzzified output is derived in case of a fuzzy logic controller with two inputs, triangular memberships, MacVicar-Whelan type linguistic rules, and direct fuzzy reasoning. As a result, it is shown that fuzzy logic controller is a nonlinear controller. Also its nonlinearity is analyzed with respect to the conventional PID control and the sliding mode control.

• Journal of Advanced Marine Engineering and Technology
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• v.20 no.2
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• pp.101-101
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• 1996

This paper presents the linearized fuzzy modeling technique of nonlinear dynamical system and the stability analysis of fuzzy control system. Firstly, the nonlinear system is partitionized by multiple linear fuzzy subcontrol systems based on fuzzy linguistic variables and fuzzy rules. Secondly, the disturbance adaptaion controllers which guarantee the global asymptotic stability of each fuzzy subsystem by an optimal feedback control law are designed and the stability analysis procedures of the total fuzzy control system using Lyapunov functions and eigenvalues are discussed in detail through a given illustrative example.

• Journal of Advanced Marine Engineering and Technology
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• v.20 no.2
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• pp.33-39
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• 1996

This paper presents the linearized fuzzy modeling technique of nonlinear dynamical system and the stability analysis of fuzzy control system. Firstly, the nonlinear system is partitionized by multiple linear fuzzy subcontrol systems based on fuzzy linguistic variables and fuzzy rules. Secondly, the disturbance adaptaion controllers which guarantee the global asymptotic stability of each fuzzy subsystem by an optimal feedback control law are designed and the stability analysis procedures of the total fuzzy control system using Lyapunov functions and eigenvalues are discussed in detail through a given illustrative example.

• Journal of Advanced Marine Engineering and Technology
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• v.28 no.2
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• pp.228-236
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• 2004

In this paper. we present a neuro-fuzzy controller which unifies both fuzzy logic and multi-layered feed forward neural networks. Fuzzy logic provides a means for converting linguistic control knowledge into control actions. On the other hand. feed forward neural networks provide salient features. such as learning and parallelism. In the proposed neuro-fuzzy controller. the parameters of membership functions in the antecedent part of fuzzy inference rules are identified by using the error back propagation algorithm as a learning rule. while the coefficients of the linear combination of input variables in the consequent part are determined by using the least square estimation method. Finally. the effectiveness of the proposed controller is verified through computer simulation for an inverted pole system.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.302-305
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• 1996

In this paper, an optimal identification method using fuzzy-neural networks is proposed for modeling of nonlinear complex systems. The proposed fuzzy-neural modeling implements system structure and parameter identification using the intelligent schemes together with optimization theory, linguistic fuzzy implication rules, and neural networks(NNs) from input and output data of processes. Inference type for this fuzzy-neural modeling is presented as simplified inference. To obtain optimal model, the learning rates and momentum coefficients of fuzz-neural networks(FNNs) and parameters of membership function are tuned using genetic algorithm(GAs). For the purpose of its application to nonlinear processes, data for route choice of traffic problems and those for activated sludge process of sewage treatment system are used for the purpose of evaluating the performance of the proposed fuzzy-neural network modeling. The show that the proposed method can produce the intelligence model w th higher accuracy than other works achieved previously.

• Journal of Advanced Marine Engineering and Technology
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• v.20 no.4
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• pp.11-19
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• 1996

Recently, fuzzy and neural network techniques have been successfully applied to control of complex and ill-defined system in a wide variety of areas, such as robot, water purification, automatic train operation system and automatic container crane operation system, etc. In this paper, we present a neuro-fuzzy controller which unifies both fuzzy logic and multi-layered feedforward neural networks. Fuzzy logic provides a means for converting linguistic control knowledge into control actions. On the other hand, feedforward neural networks provide salient features, such as learning and parallelism. In the proposed neuro-fuzzy controller, the parameters of membership functions in the antecedent part of fuzzy inference rules are identified by using the error backpropagation algorithm as a learning rule, while the coefficients of the linear combination of input variables in the consequent part are determined by using the least square estimation method. Finally, the effectiveness of the proposed controller is verified through computer simulation of an inverted pendulum system.