• 제어로봇시스템학회:학술대회논문집
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• 1990.10b
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• pp.1135-1138
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• 1990

In this paper, we study the pole assignment problem for three-dimensional systems. We transform the denominator of transfer functions of the closed-loop system into the product of three stable one-dimensional polynomials, by performing two-dimensional dynamical feedback and input transformation on the given three-dimensional systems. In the next, we consider the possibility that these two-dimensional dynamic compensators are realizable, thoroughly, and propose the counter-measure in case that they are not realizable. And, we obtain the conditions so that the closed-loop three-dimensional systems are stable. Moreover, we calculate the dynamical dimension which is necessary for the pole assigntmnt, and suggest the pole assignmnt method with the lowest dynamical dimnsion.

• Structural Engineering and Mechanics
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• v.23 no.6
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• pp.599-613
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• 2006

A method for the dynamical analysis of FE discretized uncertain linear and nonlinear structures is presented. This method is based on the moment equation approach, for which the differential equations governing the response first and second-order statistical moments must be solved. It is shown that they require the cross-moments between the response and the random variables characterizing the structural uncertainties, whose governing equations determine an infinite hierarchy. As a consequence, a closure scheme must be applied even if the structure is linear. In this sense the proposed approach is approximated even for the linear system. For nonlinear systems the closure schemes are also necessary in order to treat the nonlinearities. The complete set of equations obtained by this procedure is shown to be linear if the structure is linear. The application of this procedure to some simple examples has shown its high level of accuracy, if compared with other classical approaches, such as the perturbation method, even for low levels of closures.

• Proceedings of the Korean Nuclear Society Conference
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• 2002.05a
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• pp.176-176
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• 2002

• Journal of the Chungcheong Mathematical Society
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• v.13 no.1
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• pp.53-63
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• 2000

The inverse shadowing property of a dynamical system is an "inverse" form of the shadowing property of the system. Recently, Kloeden and Ombach proved that if an expansive system on a compact manifold has the shadowing property then it has the inverse shadowing property. In this paper, we study topological stability of the inverse shadowing dynamical systems. In particular, we show that if an expansive system on a compact manifold has the inverse shadowing property then it is topologically stable, and so it has the shadowing property.

• Structural Engineering and Mechanics
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• v.49 no.3
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• pp.329-353
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• 2014

Increasing usage of tank cars and their intrinsic instability due to sloshing of contents have caused growing maintenance costs as well as more frequent hazards and defects like derailment and fatigue of bogies and axels. Therefore, varieties of passive solutions have been represented to improve dynamical parameters. In this task, assuming 22 degrees of freedom, dynamic analysis of partially filled tank car traveling on a curved track is investigated. In order to consider stochastic geometry of track; irregularities have been derived randomly by Mont Carlo method. More over the fluid tank model with 1 degree of freedom is also presented by equivalent mechanical approach in terms of pendulum. An active suspension system for described car is designed by using linear quadratic optimal control theory to decrease destructive effects of fluid sloshing. Eventually, the performance of the active suspension system has been compared with that of the passive one and a study is carried out on how active suspension may affect the dynamical parameters such as displacements and Nadal's derailment index.

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.523-525
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• 1998

In this paper, a robust control scheme for a class of SISO nonlinear dynamical system is proposed by using output-feedback linearization method. The presented control scheme is based on the VSS control theory concept. In this control scheme, we assume that the nonlinear dynamical system is minimum phase, i.e., the relative degree of the system is r < n and zero dynamics is stable. We also assume that the states of zero dynamics are not accessible. It is shown that the global asymptotically stability is guaranted under the proposed control scheme. The feasibility of the proposed control scheme is verified through a computer simulation.

• Honam Mathematical Journal
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• v.44 no.3
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• pp.402-418
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• 2022

Noise is a fundamental factor to increased validity and regularity of spike propagation and neuronal firing in the nervous system. In this paper, we examine the stochastic version of the Izhikevich-FitzHugh neuron dynamical model. This approach is based on techniques presented by Luo and Guo, which provide a general framework for the bifurcation and stability analysis of two dimensional stochastic dynamical system as an Itô averaging diffusion system. By using largest lyapunov exponent, local and global stability of the stochastic system at the equilibrium point are investigated. We focus on the two kinds of stochastic bifurcations: the P-bifurcation and the D-bifurcations. By use of polar coordinate, Taylor expansion and stochastic averaging method, it is shown that there exists choices of diffusion and drift parameters such that these bifurcations occurs. Finally, numerical simulations in various viewpoints, including phase portrait, evolution in time and probability density, are presented to show the effects of the diffusion and drift coefficients that illustrate our theoretical results.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.05a
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• pp.191-194
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• 2001

The ozone forecasting systems have many problems because the mechanism of the ozone concentration is highly complex, nonlinear, and nonstationary. Also, the results of prediction are not a good performance so far, especially in the high-level ozone concentration. This paper describes the modeling method of the ozone prediction system using neuro-fuzzy approaches and fuzzy clustering. The dynamic polynomial neural network (DPNN) based upon a typical algorithm of GMDH (group method of data handling) is a useful method for data analysis, identification of nonlinear complex system, and prediction of a dynamical system.

• Structural Engineering and Mechanics
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• v.61 no.1
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• pp.105-116
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• 2017

In this paper, the optimal extended homotopy analysis method (OEHAM) is introduced to deal with the damped Duffing resonator driven by a van der Pol oscillator, which can be described as a complex Multi-Degree-of-Freedom (MDOF) nonlinear coupling system. Ecumenically, the exact solutions of the MDOF nonlinear coupling systems are difficult to be obtained, thus the development of analytical approximation becomes an effective and meaningful approach to analyze these systems. Compared with traditional perturbation methods, HAM is more valid and available, and has been widely used for nonlinear problems in recent years. Hence, the method will be chosen to study the system in this article. In order to acquire more suitable solutions, we put forward HAM to the OEHAM. For the sake of verifying the accuracy of the above method, a series of comparisons are introduced between the results received by the OEHAM and the numerical integration method. The results in this article demonstrate that the OEHAM is an effective and robust technique for MDOF nonlinear coupling systems. Besides, the presented methods can also be broadly used for various strongly nonlinear MDOF dynamical systems.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.2
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• pp.189-199
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• 1999

This paper presents an optimization algorithm for a stable Self Dynamic Neural Network(SDNN) using genetic algorithm. Optimized SDNN is applied to a problem of controlling nonlinear dynamical systems. SDNN is dynamic mapping and is better suited for dynamical systems than static forward neural network. The real-time implementation is very important, and thus the neuro controller also needs to be designed such that it converges with a relatively small number of training cycles. SDW has considerably fewer weights than DNN. Since there is no interlink among the hidden layer. The object of proposed algorithm is that the number of self dynamic neuron node and the gradient of activation functions are simultaneously optimized by genetic algorithms. To guarantee convergence, an analytic method based on the Lyapunov function is used to find a stable learning for the SDNN. The ability and effectiveness of identifying and controlling a nonlinear dynamic system using the proposed optimized SDNN considering stability is demonstrated by case studies.