• KSCE Journal of Civil and Environmental Engineering Research
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• v.5 no.2
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• pp.1-9
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• 1985

Two methods for the dynamic analysis of fixed offshore structures subjected to random waves are studied. They are the frequency domain method using the equivalent linearization of the nonlinear drag force, and the time domain method utilizing the Monte Carlo simulation technique for time series of random wave particle velocities and accelerations. Example analyses are carried out for two structures with different structural characteristics and for various wave conditions. A comparison has been made between the results obtained by two methods.

• Journal of KSNVE
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• v.6 no.5
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• pp.653-660
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• 1996

There have been many studies to control holonomic and/or nonholonomic systems, and nonlinear control problems. However, their approaches require complicated intermediate procedures. Using the Generalized Inverse Method derived by Udwadia and Kalaba in 1992, this study provides two applications to the control of holonomically and/or nonholonomically constrained systems. These applications illustrate the ease with which the equation by the Generalized Inverse Method can be utilized for the purpose of (a) control of highly nonlinear systems without depending on any linearization, (b) maintaining precision tracking motions with the presence of known disturbances, and (c) explicit determination of control forces under the circumstances (a) and (b).

• International Journal of Control, Automation, and Systems
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• v.4 no.1
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• pp.124-135
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• 2006

In this paper, we propose a switching control method for a class of 2nd order nonlinear systems with single input. The main idea is to switch the control law before the trajectory of the solution arrives at singular hyperplanes which are defined by the denominator of the control law. The proposed method can handle a class of nonlinear systems which is difficult to be stabilized by the existing methods such as feedback linearization, backstepping, control Lyapunov function, and sliding mode control.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1928-1933
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• 2005

This Paper is about landing control of legged robot. Body stiffness and damping is used as landing strategy of a legged robot. First, we only used stiffness control method to control legged robot landing. Second control method,sliding mode controller and feedback linearization controller is applied to enhance position control performance. Through these control algorithm, body center of gravity behaves like mass with spring & damping in vertical direction on contact regime.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.76.3-76
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• 2002

The purpose of this paper is to present a new nonlinear feedback control called AACC (Augmented automatic choosing control) for nonlinear systems. Generally, it is easy to design the optimal control laws for linear systems, but it is not so for nonlinear systems, though they have been studied for many years. One of most popular and practical nonlinear control laws is synthesized by applying a linearization method by Taylor expansion truncated at the first order and the linear optimal control method. This is only effective in a small region around the steady state point or in almost linear systems. Controllers based on a change of coordinates in differential geometry are effective in wider...

• Journal of the Korean Institute of Telematics and Electronics A
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• v.32A no.6
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• pp.67-74
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• 1995

This paper proposes the step-stress type-I censoring model for analyzing the data of accelerated life test and reducing the time of accelerated life test. In order to obtain the data of accelerated life test, the step-stress accelerated life test was run with voltage stress to CMOS Hex Buffer. The Weibull distribution, the Inverse-power-law model and Maximum likelihood method were used. The iterative procedure using modified-quasi-linearization method is applied to solve the nonlinear equation. The proposed Weibull step-stress type-I censoring model exactly estimases the life time of units, while reducting the time of accelerated life test and the equipments of test.

• Proceedings of the IEEK Conference
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• 2002.07b
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• pp.1220-1223
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• 2002

In this paper, numerical robust stability analysis method and its design are presented. L$_2$robust stability of the fuzzy system is analyzed by casting the systems into the diagonal norm bounded linear differential inclusions (DNLDI) formulation. Based on the linear matix inequality (LMI) optimization programming, a numerical method for finding the maximum stable ranges of the fuzzy feedback linarization control gains is proposed.

• ETRI Journal
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• v.29 no.4
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• pp.536-538
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• 2007

We propose and describe the fabrication of a linear power amplifier (LPA) using a new analog feedforward method for the IMT-2000 frequency band (2,110-2,170 MHz). The proposed analog feedforward circuit, which operates without a pilot tone or a microprocessor, is a small and simple structure. When the output power of the fabricated LPA is about 44 dBm for a two-tone input signal in the IMT-2000 frequency band, the magnitude of the intermodulation signals is below -60 dBc and the power efficiency is about 7%. In comparison to the fabricated main amplifier, the magnitude of the third intermodulation signal decreases over 24 dB in the IMT-2000 frequency band.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.3
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• pp.263-272
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• 2000

In this paper we give some geometric condition for a stochastic nonlinear system and we propose a control method for a stochastic nonlinear system using neural networks. Since a competitive learning neural networks has been developed based on the stochastcic approximation method it is regarded as a stochastic recursive filter algorithm. In addition we provide a filtering and control condition for a stochastic nonlinear system called the perfect filtering condition in a viewpoint of stochastic geometry. The stochastic nonlinear system satisfying the perfect filtering condition is decoupled with a deterministic part and purely semi martingale part. Hence the above system can be controlled by conventional control laws and various intelligent control laws. Computer simulation shows that the stochastic nonlinear system satisfying the perfect filtering condition is controllable and the proposed neural controller is more efficient than the conventional LQG controller and the canonical LQ-Neural controller.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.1
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• pp.72-81
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• 2009

This paper presents a robust adaptive control method using model reference control strategy against autonomous robot systems with random friction nature. We approximate a nonlinear robot system model by means of a feedback linearization approach to derive nominal control law. We construct a Least Square (LS) based observer to estimate friction dynamics online and then represent a perturbed system model with respect to approximation error between an actual friction and its estimation. Model reference based control design is achieved to implement an auxiliary control in order for reducing control error in practice due to system perturbation. Additionally, we conduct theoretical study to demonstrate stability of the perturbed system model through Lyapunov theory. Numerical simulation is carried out for evaluating the proposed control methodology and demonstrating its superiority by comparing it to a traditional nominal control method.