• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.131-134
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• 1997

This paper presents a new approach to the design of intelligent control system for track vehicle system using fuzzy logic based on neural network. The proposed control scheme uses a Gaussian function as a unit function in the neural network-fuzzy, and back propagation algorithm to train the fuzzy-neural network controller in the framework of the specialized learning architecture. It is proposed a learning controller consisting of two neural network-fuzzy based on independent reasoning and a connection net with fixed weights to simply the neural networks-fuzzy. The performance of the proposed controller is illustrated by simulation for trajectory tracking of track vehicle speed.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.4
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• pp.499-503
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• 2003

This paper we study the exact controllability for the nonlinear fuzzy control system in $E_N^n$by using the concept of fuzzy number of dimension n whose values are normal, convex, upper semicontinuous and compactly supported surface in $E_N^n$

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.194-198
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• 1998

A novel fuzzy basis function vector- based adaptive control approach for Multi-input and Multi-output(MIMO) system is presented in this paper, in which the nonlinear plants is first linearised, the fuzzy basis function vector is then introduced to adaptively learn the upper bound of the system uncertainty vector, and its output is used as the paramenters of the compensator in the sense that both the robustness and the asymptotic error convergence can be obtained for the closed loop nonlinear control system.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.6
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• pp.91-98
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• 1998

A DEDS is a system whose stated change in response to the occurrence of events from a predefined event set. A major difficulty in developing analytical results for the system is the lack of appropriate modeling techniques. In this paper, we consider the modeling and control problem for Discrete Event Dynamic Systems(DEDS) in the Temporal Logic framework(TLF) which have been recently defined. The traditional TLF is enhanced with time functions for real time control of Discrete Event Dynamic Systems. A sequence of event which drive the system from a given initial state to a given final state is generated by pertinently operating the given plants. This paper proposes the use of Real-time Temporal Logic as a modeling tool for the analysis and control of DEDS. An given example of fixed-time traffic control problem is shown to illustrate our results with Real-time Temporal Logic Framework.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.3
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• pp.143-148
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• 2011

In this paper, a backstepping design is proposed to achieve stabilization and synchronization for the Lorenz-Stenflo (LS) chaotic system. The proposed method is a recursive Lyapunov-based scheme and provides a systematic procedure to design stabilizing controllers. The proposed controller enables stabilization of the chaotic motion and synchronization of two identical LS chaotic systems using only a single control input. Numerical simulations are presented to validate the proposed method.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.1
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• pp.12-18
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• 2011

This paper deals with a new adaptive fuzzy sliding mode controller and its application to an inverted pendulum. We propose a new method of adaptive fuzzy sliding mode control scheme that the fuzzy logic system is used to approximate the unknown system functions in designing the SMC of uncertain nonlinear systems. The controller's construction and its analysis involve sliding modes. The proposed controller consists of two components. Sliding mode component is employed to eliminate the effects of disturbances, while a fuzzy model component equipped with an adaptation mechanism reduces modeling uncertainties by approximating model uncertainties. To demonstrate its performance, the proposed control algorithm is applied to an inverted pendulum. The results show that both alleviation of chattering and performance are achieved.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2002.04a
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• pp.381-386
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• 2002

In this paper, we have proposed a new approach to the design of robot vision system to develop the technology for the automatic test and assembling of precision mechanical and electronic parts for the factory automation. In order to perform real time implementation of the automatic assembling tasks in the complex processes, we have developed an intelligent control algorithm based-on neural networks control theory to enhance the precise motion control. Implementing of the automatic test tasks has been performed by the real-time vision algorithm based-on TMS320C31 DSPs. It distinguishes correctly the difference between the acceptable and unacceptable defective item through pattern recognition of parts by the developed vision algorithm. Finally, the performance of proposed robot vision system has been illustrated by experiment for the similar model of fifth cell among the twelve cell for automatic test and assembling in S company.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.341-345
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• 1997

A novel fuzzy basis function vector-based adaptive control approach for Multi-input and Multi-output(MIMO) system is presented in this paper, in which the nonlinear plants is first linearised, the fuzzy basis function vector is then introduced to adaptively learn the upper bound of the system uncertainty vector, and its output is used as the parameters of the compensator in the sense that both the asymptotic error convergence can be obtained for the colsed loop nonlinear control system.

• Journal of the Korean Institute of Intelligent Systems
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• v.5 no.3
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• pp.3-10
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• 1995

This paper proposedan on-line learning controller which can be applied to nonlinear systems. The proposed on-line learning controller is based on the universal approximation by the local affine mapping-based neural networks. It has self-organizing and learning capability to adapt itself to the new environment arising from the variation of operating point of the nonlinear system. Since the learning controller retains the knowledge of trained dynamics, it can promptly adapt itself to situations similar to the previously experienced one. This prompt adaptability of the proposed control system is illustrated through simulations.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2001.04a
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• pp.217-222
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• 2001

In this paper, we have proposed a new approach to the design of robot vision system to develop the technology for the automatic test and assembling of precision mechanical and electronic parts for the factory automation. In order to perform real time implementation of the automatic assembling tasks in the complex processes, we have developed an intelligent control algorithm based-on neural networks control theory to enhance the precise motion control. Implementing of the automatic test tasks has been performed by the real-time vision algorithm based-on TMS320C31 DSPs. It distinguishes correctly the difference between the acceptable and unacceptable defective item through pattern recognition of parts by the developed vision algorithm. Finally, the performance of proposed robot vision system has been illustrated by experiment for the similar model of fifth cell among the twelve cell for automatic test and assembling in S company.