• Structural Engineering and Mechanics
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• v.6 no.5
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• pp.473-484
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• 1998

The first and second moments of response variables for SDOF systems with hysteretic nonlinearity are obtained by a direct linearization procedure. This adaptation in the implementation of well-known statistical linearization methods, provides concise, model-independent linearization coefficients that are well-suited for numerical solution. The method may be applied to systems which incorporate any hysteresis model governed by a differential constitutive equation, and may be used for zero or non-zero mean random vibration. The implementation eliminates the effort of analytically deriving specific linearization coefficients for new hysteresis models. In doing so, the procedure of stochastic analysis is made independent from the task of physical modeling of hysteretic systems. In this study, systems with three different hysteresis models are analyzed under various zero and non-zero mean Gaussian White noise inputs. Results are shown to be in agreement with previous linearization studies and Monte Carlo Simulation.

• Journal of the Society of Naval Architects of Korea
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• v.32 no.1
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• pp.37-41
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• 1995

In order to analyze the rolling motion of a ship in random beam waves we use the partial stochastic linearization method. The quadratic damping and the nonlinear restoring moments given by the odd polynomials up to the 11th order are added to a single degree of freedom linear equation of roll motion. The irregular excitation moment is assumed to be the Gaussian white noise. The statistical characteristics of the response by the partial stochastic linearization method is compared with results by the equivalent linearization method and Monte Carlo simulation. It is fecund that the partial stochastic linearization method is not necessarily superior to the equivalent linearization method.

• Proceedings of the KIEE Conference
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• 2001.11c
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• pp.18-21
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• 2001

In this paper, some geometric condition for a stochastic nonlinear system and an adaptive control method for minimum-phase stochastic nonlinear system using neural network me provided. The state feedback linearization is widely used technique for excluding nonlinear terms in nonlinear system. However, in the stochastic environment, even if the minimum phase linear system derived by the feedback linearization is not sufficient to be controlled robustly. In the viewpoint of that, it is necessary to make an additional condition for observation of nonlinear stochastic system, called perfect filtering condition. In addition, on the above stochastic nonlinear observation condition, I propose an adaptive control law using neural network. Computer simulation shoo's that the stochastic nonlinear system satisfying perfect filtering condition is controllable and the proposed neural adaptive controller is more efficient than the conventional adaptive controller.

• Transactions of the Korean Society of Mechanical Engineers
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• v.14 no.6
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• pp.1426-1435
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• 1990

Dynamic behaviour of point tracking system mounted on flying vehicle shaking in a random manner is investigated and the system dynamic is represented by nonlinear stochastic equations. 2-D.O.F. nonlinear stochastic equations are successfully transformed to linear stochastic equations via equivalent linearization procedure in stochastic sense. Newly developed hybrid technique is used to obtain response statistics of the system under non-white random excitation, which is proved to be remarkably accurate one by performing stochastic simulation.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.18-18
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• 2000

In this paper, some geometric condition for a stochastic nonlinear system and an adaptive control method for minimum-phase stochastic nonlinear system using neural network are provided. The state feedback linearization is widely used technique for excluding nonlinear terms in nonlinear system. However, in the stochastic environment, even if the minimum phase linear system derived by the feedback linearization is not sufficient to be controlled robustly. the viewpoint of that, it is necessary to make an additional condition for observation of nonlinear stochastic system, called perfect filtering condition. In addition, on the above stochastic nonlinear observation condition, I propose an adaptive control law using neural network. Computer simulation shows that the stochastic nonlinear system satisfying perfect filtering condition is controllable and the proposed neural adaptive controller is more efficient than the conventional adaptive controller

• Earthquakes and Structures
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• v.14 no.5
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• pp.477-492
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• 2018

Recently, the adaptive nonlinear static analysis method has been widely used in the field of performance based earthquake engineering. However, the proposed methods are almost deterministic and cannot directly consider the seismic record uncertainties. In the current study an innovative Stochastic Adaptive Pushover Analysis, called "SAPA", based on equivalent hysteresis system responses is developed to consider the earthquake record to record uncertainties. The methodology offers a direct stochastic analysis which estimates the seismic demands of the structure in a probabilistic manner. In this procedure by using a stochastic linearization technique in each step, the equivalent hysteresis system is analyzed and the probabilistic characteristics of the result are obtained by which the lateral force pattern is extracted and the actual structure is pushed. To compare the results, three different types of analysis have been considered; conventional pushover methods, incremental dynamic analysis, IDA, and the SAPA method. The result shows an admirable accuracy in predicting the structure responses.

• Structural Engineering and Mechanics
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• v.64 no.2
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• pp.259-270
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• 2017

The stochastic vibration response of the sandwich beam with the nonlinear adjustable visco-elastomer core and supported mass under stochastic support motion excitations is studied. The nonlinear dynamic properties of the visco-elastomer core are considered. The nonlinear partial differential equations for the horizontal and vertical coupling motions of the sandwich beam are derived. An analytical solution method for the stochastic vibration response of the nonlinear sandwich beam is developed. The nonlinear partial differential equations are converted into the nonlinear ordinary differential equations representing the nonlinear stochastic multi-degree-of-freedom system by using the Galerkin method. The nonlinear stochastic system is converted further into the equivalent quasi-linear system by using the statistic linearization method. The frequency-response function, response spectral density and mean square response expressions of the nonlinear sandwich beam are obtained. Numerical results are given to illustrate new stochastic vibration response characteristics and response reduction capability of the sandwich beam with the nonlinear visco-elastomer core and supported mass under stochastic support motion excitations. The influences of geometric and physical parameters on the stochastic response of the nonlinear sandwich beam are discussed, and the numerical results of the nonlinear sandwich beam are compared with those of the sandwich beam with linear visco-elastomer core.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.40 no.2
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• pp.108-117
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• 2012

This paper focuses on a performance analysis of TRN among various nonlinear filtering methods. In a TRN research, extended Kalman filter(EKF) is a basic estimation algorithm. In this paper, iterated EKF(IEKF), EKF with stochastic linearization(SL), and unscented Kalman filter(UKF) algorithms are introduced to compare navigation performance with original EKF. In addition to introduced sequential filters, bank of Kalman filters method, which is one of the batch method, is also presented. Finally, by simulating an artificial aircraft mission, EKF with SL was chosen as the most consistent filter in the introduced sequential filters. Also, results suggested that the bank of Kalman filters can be alternative for TRN, when a fast convergence of navigation solution is needed.

• Computers and Concrete
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• v.9 no.3
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• pp.195-213
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• 2012

This paper presents the investigation of the stochastic responses of seismically isolated bridges subjected to spatially varying earthquake ground motions including incoherence, wave-passage and site-response effects. The incoherence effect is examined by considering Harichandran and Vanmarcke coherency model. The effect of the wave-passage is dealt with various wave velocities in the response analysis. Homogeneous firm, medium and soft soil conditions are selected for considering the site-response effect where the bridge supports are constructed. The ground motion is described by filtered white noise and applied to each support points. For seismic isolation of the bridge, single and double concave friction pendulum bearings are used. Due to presence of friction on the concave surfaces of the isolation systems, the equation of motion of is non-linear. The non-linear equation of motion is solved by using equivalent linearization technique of non-linear stochastic analyses. Solutions obtained from the stochastic analyses of non-isolated and isolated bridges to spatially varying earthquake ground motions compared with each other for the special cases of the ground motion model. It is concluded that friction pendulum systems having single and double concave surfaces have important effects on the stochastic responses of bridges to spatially varying earthquake ground motions.

• Proceedings of the KIEE Conference
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• 1990.07a
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• pp.72-76
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• 1990

A new statistical linearization technique for nonlinear system called covariance matching method is proposed in this paper. The covariance matching method makes the mean and variance of an approximated output be identical real functional output, and the distribution of the approximated output have identical shape with a given random input. Also, the covariance matching method can be easily implemented for statistical analysis of nonlinear systems with a combination of linear system covariance analysis.