• 제어로봇시스템학회:학술대회논문집
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• 1990.10b
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• pp.1355-1360
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• 1990

This paper describes the application of the recently developed feedback linearization technique to the design of a new command to line-of-sight (CLOS) guidance law for skid-to-turn (STT) missiles. The key idea lies in converting the three dimensional CLOS guidance problem to the tracking problem of a time-varying nonlinear system. Then, using a feeedback linearizing approach to tracking in nonlinear systems, we design a three dimensional CLOS guidance law that can ensure zero miss distance for a randomly maneuvering target. Our result may shed new light on the role of the feedforward acceleration terms used in the earlier CLOS guidance laws. Furthermore, we show that the new CLOS guidance law can be computationally simplified without performance degradation. This is made possible by dropping out the terms in the new CLOS guidance law, which obey the well-known matching condition.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.583-588
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• 1989

In this paper, a modelling methodology for a robotic workcell is proposed and compared with the conventional Petri nets model. Also, a method for managing the cell operation is described through the knowledge base. The knowledge bases for state transition and assembly job information are obtained from the state transition map(STM) and the assembly job tree(AJT), respectively. Using the knowledge base, the system structure is discussed in both managing the cell operation and evaluating the various performance. Finally, a simulation algorithm is presented with the simulation results to show the effectiveness of the proposed modelling approach.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.440-443
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• 1995

A control strategy for random parametic system by using FLC is investigated and developed. In the course of research, as a first part, nonlinear system under random disturbance is investigated. Preliminary results is presented in the paper. A control technique, GA based FLC, is employed successfully for inverted pendulum experiencing white noise excitation.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.1041-1046
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• 1989

In this paper, we attempt to control induction motors with high power efficiency as well as high dynamic performance by utilizing the recently developed theories: singular perturbation technique and noninteracting feedback control. Our controller does not need the transformation between a d-q synchronously rotating frame and a x-y stator-fixed frame. It is computationally quite simple. Furthermore, it does not depend on the rotor resistance. To illuminate the practical significance of our results, we present simulation and experimental results as well as mathematical performance analysis.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.496-499
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• 2003

Support Vector Machines (SVMs) is applied to a practical problem as one of standard tools for machine learning. The application of Reinforcement Learning (RL) and SVMs in action of mobile robot is investigated. A technique to decide the action of autonomous mobile robot in practice is explained in the paper, The proposed method is to find n basis for good action of the system under unknown environment. In multi-dimensional sensor input, the most reasonable action can be automatically decided in each state by RL. Using SVMs, not only optimal decision policy but also generalized state in unknown environment is obtained.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.838-840
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• 1999

In this paper, modeling and analysis of discrete event systems by temporal logic frame works and petri net is considered. The reachability tree of the petri net can be used to solve the safeness, boundedness, conservation and coverability problems of discrete event systems. But the reachability tree of the petri net do not solve reachability and liveness problems in general. We proposed a method that synthesised the petri net and the temporal logic frameworks. This method slove some problems of petri net by logical representation of temporal logic frameworks.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.3001-3003
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• 2000

In this paper. we propose Fuzzy Self-Organizing Networks (FSON) using both Polynomial Neural Networks(PNN) and Fuzzy Neural Networks(FNN) for model identification of complex and nonlinear systems. The proposed FSON is generated from the mutually combined structure of both FNN and PNN. Accordingly it is possible to consider the nonlinearity characteristics of process and to get the better output performance with superb predictive ability. In order to evaluate the performance of proposed models. we use the nonlinear data sets. The results show that the proposed FSON can produce the model with higher accuracy and more robustness than previous any other method.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1182-1185
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• 1996

The objective of this paper is to control a nonlinear dynamical systems based on Self-Organized Distributed Networks (SODN). The learning with the SODN is fast and precise. Such properties are caused from the local learning mechanism Each local network learns only data in a subregion. Methods for indirect adaptive control of nonlinear systems using the SODN is presented. Through extensive simulation, the SODN is shown to be effective for adaptive control of nonlinear dynamic systems.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.82-87
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• 1993

A supervisory controller design technique for multiple-AGV systems is presented in this paper. The guidepath is represented in the form of a network, and its modifications are easily tested. The network has two-layered structure, where the path sets between each two nodes are made in advance using the K-shortest path algorithm. Occupation times for all links are stored in link-occupation table, and are updated after the dispatching time. Dispatching and scheduling for each AGV are optimized in terms of minimum-time objectives. In all times, the paths are guaranteed to be conflict-free and deadlock-free. The simplicity and flexibility on this control scheme make the supervisory suitable for real applications.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.2
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• pp.121-126
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• 2006

In the recent years, the development of computer-controlled suspension dampers and actuators has improved the trade-off between the vehicle handling and ride comfort, and has led to the development of various damper control policies. The skyhook control is an effective control strategy for suppressing vehicle vibration. In this study, a fuzzy skyhook control is proposed and tuned by a genetic algorithm to improve ride comfort. The proposed fuzzy skyhook control is applied to a quarter-car model in order to compare its performance with continuous skyhook suspensions. To obtain optimized fuzzy skyhook control, scale factors and in-out membership functions are tuned by a genetic algorithm. The simulation results show that the fuzzy skyhook control offers more effective suspension performance over the continuous skyhook control.