• Journal of the Korea Institute of Military Science and Technology
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• v.11 no.3
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• pp.58-66
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• 2008

In this paper, we have proposed a method and procedure which can find out the unknown network protocol. Although it seems to be difficult to identify the protocol, we can find out the rule in the packet according to the method we have proposed. We have to recognize functions of the system and make the list of events first. Then we capture the network packet whenever the event are occurred. The captured packets are examined by means of the method that is finding repeated parts, changed parts according to the input value, fixed parts and changed parts according to regular rules. Finally we make the test program to verify the protocol. We applied this method and procedure to upgrade Electronic Warfare Test System which is operated by ADD. We have briefly described the redesign of control and analysis software for Electronic Warfare Test System

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.2
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• pp.153-158
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• 1999

We consider single-input nonlinear systems with unknown unmodelled time-varying parameters or disturbances which are bounded. The main goal is to identify classes of uncertain systems for which the control exist and to provide constructive design procedures. Assuming that the undisturbed nominal system ( ,g) is partially state feedback linearizable, that a strict triangularity condition, a linear parametrization condition, and {{{{ { G}_{r-1 } }}}} hold for the uncertain terms, and that some condition is satisfied in the transformed partially linear system, we design an adaptive regulating dynamic control. At first, we identify classes of nonlinear uncertain systems and give a systematic procedure for the design of a robust regulation for the nonlinear systems.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.41 no.3
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• pp.311-319
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• 1992

In this paper, a direct adaptive pole placement controller for an unknown linear time-invariant single-input single-output nonminimum phase plant is proposed. To design this direct adaptive pole placement controller, the auxiliary signals are introduced. Consequently, a linear equation error model is formulated for estimating both the controller parameters and the additional auxiliary parameters. To estimate the controller parameters and the additional auxiliary parameters, the exponentially weighted least-squares algorithm is implemented, and a method of selecting the characteristic polynomials of the sensitivity function filters is proposed. In this method, all the past measurement data are weighted exponentially. A series of simulations for a nonminimum phase plant is presented to illustrate some features of both the parameter estimation and the output response of this adaptive pole placement controller.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.381-381
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• 2000

This paper describes the path planning method in an unknown environment for an autonomous mobile robot equipped with CCD(Charge-Coupled Device) camera. The mobile robot moves along the guideline. The CCD camera is useful to detect the existence of a guideline. The wavelet transform is used to find the edge of guideline. Using wavelet transform, we can make an image processing more easily and rapidly. We make a fuzzy control rule using image data then make a decision the position and the navigation of the mobile robot. The center value that indicates the center of guideline is the input of fuzzy logic controller and the steering angle of the mobile robot is the fuzzy output. Some actual experiments for the mobile robot applied fuzzy control show that the mobile robot effectively moves to target position.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.04b
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• pp.262-266
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• 1995

This paper presents a new adaptive-neuro control scheme to control the velocity and position of SCARA robot with parameter uncertainties. The adaptive control of linear system found wiedly in many areas of control application. While techniques for the adaptive control of linear systems have been well-established in the literature, there are a few corresponding techniques for nonlinear systems. In this paper an attempt is made to present a newcontrol scheme for theadaptive control of ponlinear robot based on a feedforward neural network. The proposed approach incorporates a neuro controller used within a reinforcement learning framework, which reduces the problem to one of learning a stochastic approximation of an unknown average error surface Emphasis is focused on the fact that the adaptive-neuro controoler dose not need any input/output information about the controlled system. The simulation result illustrates the effectiveness of the proposed adaptive-neuro control scheme.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.7 s.172
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• pp.94-101
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• 2005

This work proposes a new method for describing the hysteresis non-linearity of a piezoelectric actuator. The hysteresis behaviour of piezoelectric actuators, including the minor loop trajectory, are modeled by geometrical relationship between a reference major loop and its minor loops. This hysteresis model is transformed into inverse hysteresis model in order to output compensated voltage with regard to the given input displacement. A feedforward neural network, which is trained by a feedback PID control module, is incorporated to the inverse hysteresis model to compensate unknown dynamics of the piezoelectric system. To show the feasibility of the proposed feedforward-feedback controller, some experiments have been carried out and the tracking performance was compared to that of simple PTD controller.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.17 no.10
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• pp.918-922
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• 2007

Most of the study on system identification has been carried out using input/output relation in physical domain. However identification concept of stochastic system has not been reported up to now. Interest is focused to identify an unknown dynamic system under random external disturbances which is not possible to measure. A concept to identify the system parameters in stochastic domain is proposed and implemented in terms of simulation. Attempt has been made to identify the system parameters in inverse manner in stochastic domain based on system output only. Simulation is conducted to reveal quite noticeable performance of the proposed concept.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.3
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• pp.211-217
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• 2000

A main contribution of this paper is the development of a Hopfield network-based algorithm for the fault diagnosis of the actuators in linear system with uncertainties. An unknown input decoupling approach is introduced to the design of an adaptive observer so that the observer is insensitive to uncertainties. As a result, the output observation error equation does not depend on the effect of uncertainties. Simultaneous energy minimization by the Hopfield network is used to minimize the least mean square of errors of errors of estimates of output variables. The Hopfield network provides an estimate of the gains of the actuators. When the system dynamics changes, identified gains go through a transient period and this period is used to detect faults. The proposed scheme is demonstrated through its application to a simulated second-order system.

• Journal of Industrial Technology
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• v.20 no.B
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• pp.35-43
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• 2000

Time Delay Control (TDC) method was proposed as a promising technique in the robust control area, where the plants have unknown dynamics with parameter variations and substantial disturbances are present. In this paper, based on the concepts of TDC, author propose a model reference control method for input/output model. The stability and robustness of the closed system has been analyzed for a class of linear time invarient (LTI) system. Then, in a simulation study, author's design method has been applied to a second order system, the result of which confirmed that the proposed control method performs satisfactorily as predicted.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.05a
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• pp.144-147
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• 2001

This paper presents adaptive fuzzy controller which is uncertainty or unknown variation in different parameters with nonlinear system of helicopter. The proposed adaptive fuzzy controller applied TSK(Takagi-Sugeno-Kang) fuzzy system which is not only low number of fuzzy rule, and a linear input-output equation with a constant term, but also can represent a large class of nonlinear system with good accuracy. The adaptive law was designed by using Lyapunov stability theory. The adaptive fuzzy controller is a model reference adaptive controller which can adjust the parameter $\theta$ so that the plant output tracks the reference model output. First of all, system of helicopter was considered as stopping state, and design of controller was simulated from dynamics equation with stopping state. Results show that it is controlled more successfully with a model reference adaptive controller than with a non-adaptive fuzzy controller when there is a modelling error between system and model or a continuous added noise in such unstable system.