• Wind and Structures
• /
• v.15 no.3
• /
• pp.209-221
• /
• 2012

The flutter reliability analysis of long span bridges requires use of a software tool that predicts the uncertainty in a flutter response due to uncertainties in the model formulation and input parameters. Existing flutter analysis numerical codes are not capable of dealing with stochastic uncertainty in the analysis of long span bridges. The goal of the present work is to develop a software tool (FREASB) to enable designers to efficiently and accurately conduct flutter reliability analysis of long span bridges. The FREASB interfaces an open-source Matlab toolbox for structural reliability analysis (FERUM) with a typical deterministic flutter analysis code. The paper presents a brief introduction to the generalized first-order reliability method implemented in FREASB and key steps involved in coupling it with a typical deterministic flutter analysis code. A numerical example concerning flutter reliability analysis of a long span suspension bridge with a main span of 1385 m is presented to demonstrate the application and effectiveness of the methodology and the software.

• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.40 no.1
• /
• pp.11-18
• /
• 2003

We consider a single-input-single-output nonlinear system which is represented in a normal form. The model contains the uncertainty. A high-gain observer is used to estimate the states variables to reject a modeling uncertainty We design the globally bounded output feedback controller using sliding mode control to stabilize the closed-loop system. The globally bounded output feedback controller reduce the peaking in the states variables. The proposed method give a more design freedom in the design of the globally bounded controller than that of the previous work.

• Proceedings of the KIEE Conference
• /
• 2001.07d
• /
• pp.2096-2098
• /
• 2001

Fuzzy control has been researched for application of industrial processes which have no accurate mathematical model and could not be controlled by conventional methods because of a lack of quantitative input-output data. Intelligent control approach based on fuzzy logic could directly reflex human thinking and natural language to controller comparing with conventional methods. In this paper, the tested system is constructed for sending a ball to the goal position using wind from two DC motors in the path. This system contains non-linearity and uncertainty because of the characteristic of aerodynamics inside the path. The system used in this experiment could be hardly modeled by mathematic methods and could not be easily controlled by linear control manners. The controller, in this paper could control the system containing non-linearity and uncertainty.

• Journal of the Korean Society for Precision Engineering
• /
• v.12 no.4
• /
• pp.127-134
• /
• 1995

The dynamic characteristics of a system can be critically influenced by system uncertainty, so the dynamic system must be analyzed stochastically in consideration of system uncertainty. This study presents a generalized stochastic model of dynamic system subjected to bot external and parametric nonstationary stochastic input. And this stochastic system is analyzed by a new stochastic process closure method and moment equation method. The first moment equation is numerically evaluated by Runge-Kutta method. But the second moment equation is founded to constitute an infinite coupled set of differential equations, so this equations are numerically evaluated by cumulant neglect closure method and Runge-Kutta method. Finally the accuracy of the present method is verified by Monte Carlo simulation.

• 제어로봇시스템학회:학술대회논문집
• /
• 1994.10a
• /
• pp.359-363
• /
• 1994

In this paper the system called the inverse model compensation system is proposed as a system whose input-output transfer function can be regarded as that of a model with uncertainty in spite of including an unknown plant. And their to construct the robust model following system, which is of low sensitivity and robust stability, in order to control the inverse model compensation system is proposed. The simulation experiments show that the robust model following system including the inverse model compensation system is practical and useful as a system which controls unknown plants.

• Ecology and Resilient Infrastructure
• /
• v.9 no.1
• /
• pp.24-35
• /
• 2022

For water quality management, it is necessary to continuously improve the forecasting by analyzing the past water quality, and a Data-driven model is emerging as an alternative. Because the Data-driven model is built based on a wide range of data, it is essential to apply the correlation analysis method for the combination of input variables to obtain more reliable results. In this study, the Gamma Test was applied as a preceding step to build a faster and more accurate data-driven water quality prediction model. First, a physical-based model (HSPF, EFDC) was operated to produce daily water quality reflecting the complexity of the watershed according to various hydrological conditions for Paldang Dam. The Gamma Test was performed on the water quality at the water quality prediction site (Paldangdam2) and major rivers flowing into the Paldang Dam, and the method of selecting the optimal input data combination was presented through the analysis results (Gamma, Gradient, Standar Error, V-Ratio). As a result of the study, the selection criteria for a more efficient combination of input data that can save time by omitting trial and error when building a data-driven model are presented.

• Proceedings of the Korea Institute of Convergence Signal Processing
• /
• 2000.12a
• /
• pp.221-224
• /
• 2000

We consider one of robust controller, fuzzy-sliding mode controller dealing with model uncertainty, simplified representation of nonlinear system, changed parameters of plant. We propose fuzzy-sliding mode algorithm which provides control input that has system states approaching the choosed sliding surface. This fuzzy controller has a rule base to get initial states converged on sliding surface. This algorithm Is applied to a transfer function of DC motor to be modeled simply and do position control of DC motor due to system parameters. We compare fuzzy-sliding mode controller to both sliding mode controller and fuzzy controller to identify roust control.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2002.12a
• /
• pp.84-87
• /
• 2002

This paper presents an adaptive PID control scheme with fuzzy model for nonlinear system. TSK(Takagi-Sugeno-Kang) fuzzy model was used to estimate the error of control input, and the parameter of PID controller was adapted from the error The parameter of TSK fuzzy model was also adapted to plant by comparing the activity output of plant and model output. PID controller which was adapted the uncertainty of nonlinear plant and the change of parameter can be designed by using the presented method. The usefullness of algorithm which was proposed by the simulation of several nonlinear system was also certificated.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.23 no.6
• /
• pp.641-649
• /
• 2010

Estimation of uncertain parameters is required in many engineering problems which involve probabilistic structural analysis as well as prognosis of existing structures. In this case, Bayesian framework is often employed, which is to represent the uncertainty of parameters in terms of probability distributions conditional on the provided data. The resulting form of distribution, however, is not amenable to the practical application due to its complex nature making the standard probability functions useless. In this study, Markov chain Monte Carlo (MCMC) method is proposed to overcome this difficulty, which is a modern computational technique for the efficient and straightforward estimation of parameters. Three case studies that implement the estimation are presented to illustrate the concept. The first one is an inverse estimation, in which the unknown input parameters are inversely estimated based on a finite number of measured response data. The next one is a metamodel uncertainty problem that arises when the original response function is approximated by a metamodel using a finite set of response values. The last one is a prognostics problem, in which the unknown parameters of the degradation model are estimated based on the monitored data.

• Journal of Information Processing Systems
• /
• v.16 no.3
• /
• pp.588-598
• /
• 2020

An alternative state estimation filtering algorithm is designed for continuous time systems with noises as well as control input. Two kinds of estimation filters, which have different measurement memory structures, are operated selectively in order to use both filters effectively as needed. Firstly, the estimation filter with infinite memory structure is operated for a certain continuous time system. Secondly, the estimation filter with finite memory structure is operated for temporarily uncertain continuous time system. That is, depending on the presence of uncertainty, one of infinite memory structure and finite memory structure filtered estimates is operated selectively to obtain the valid estimate. A couple of test variables and declaration rule are developed to detect uncertainty presence or uncertainty absence, to operate the suitable one from two kinds of filtered estimates, and to obtain ultimately the valid filtered estimate. Through computer simulations for a continuous time aircraft engine system with different measurement memory lengths and temporary model uncertainties, the proposed state estimation filtering algorithm can work well in temporarily uncertain as well as certain continuous time systems. Moreover, the proposed state estimation filtering algorithm shows remarkable superiority to the infinite memory structure filtering when temporary uncertainties occur in succession.