• Journal of Institute of Control, Robotics and Systems
• /
• v.2 no.4
• /
• pp.297-305
• /
• 1996

In this paper a novel robust adaptive fuzzy controller for the nonlinear system with state-dependent uncertainty is proposed. The conventional adaptive fuzzy controller determines the function of state variable bounding the state-dependent uncertain term in the system dynamics on the local state space by off-line calculation. Whereas the proposed method determines that function by the fuzzy inference so that it guarantees the stability of the closed loop system globally on the whole state space. In addition, the method is applicable to the multi-input system. We applied the proposed method to the Burn Control of the Tokamak fusion reactor whose dynamics contains the state-dependent uncertainty and proved the effectiveness of the scheme by using the simulation results.

• Journal of the Korean Society for Precision Engineering
• /
• v.15 no.12
• /
• pp.128-141
• /
• 1998

We propose the robust nonlinear controller design methodology for the multivariable system which has hard nonlinearities (Coulomb friction, dead-zone, etc) and the structured real parameter uncertainty. The hard nonlinearity can be linearized by the RIDF technique and structured real parameter uncertainty can be modelled as the sense of Peterson-Hollot's quadratic Lyapunov bound. For this system, we apply the robust QLQG/H$_{\infty}$ control and then can obtain four Riccati equations. Because of the system's nonlinearity, however, one Riccati equation contains the nonlinear correction term that is very difficult to solve numerically, In order to treat this problem, using some transformations to Riccati equations, the nonlinear correction term can be eliminated. Then, only two Riccati equations need to design a controller. Finally, the robust nonlinear controller is synthesized via IRIDF techniques. To test this proposed control method, we consider the direct-drive robot manipulator system that has Coulomb frictions and varying inertia.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.15 no.5
• /
• pp.949-958
• /
• 2020

The functions of shock absorbers are to dampen body, suspend motions, dissipate impact energy, and control tire force variation. During the operation, hydraulic oil is passed between the chambers via a flow restrictions. Therefore the damping force characteristics of shock absorber is determined by the characteristics of orifices and flow restrictions. The uncertainty in design variable affects the performance of suspension system strongly. But, the researches about the influence of uncertainty in design variable such as a fluid restriction's property of shock absorber, on the suspension system performance was hardly ever proposed. In this paper, we used statistical method of Latin Hypercube sampling, and the effects of design variables uncertainty on the performance of suspension system was presented.

• Journal of Electrical Engineering and Technology
• /
• v.9 no.1
• /
• pp.60-70
• /
• 2014

This paper discusses the impacts of large scale grid-connected wind farm equipped with permanent magnet synchronous generator (PMSG) on power system small signal stability (SSS) incorporating wind generation uncertainty and volatility. Firstly, a practical simplified PMSG model with rotor-flux-oriented control strategy applied is derived. In modeling PMSG generator side converter, the generator-voltage-oriented control strategy is utilized to implement the decoupled control of active and reactive power output. In modeling PMSG grid side converter, the grid-voltage-oriented control strategy is applied to realize the control of DC link voltage and the reactive power regulation. Based on the Weibull distribution of wind speed, the Monte Carlo simulation technique based is carried out on the IEEE 16-generator-68-bus test system as benchmark to study the impacts of wind generation uncertainty and volatility on small signal stability. Finally, some preliminary conclusions and comments are given.

• The KSFM Journal of Fluid Machinery
• /
• v.17 no.4
• /
• pp.5-10
• /
• 2014

It is possible for the nation's largest flowmeter calibration system in K-water to calibrate flow rate up to $2,700m^3/h$ and diameter 800mm. However, the calibration and measurement capability of K-water's system is not satisfied in comparison with other developed countries. In this study, we find the dominant factors related to the uncertainty of weight and time measurement for gravimetric flowmeter calibration system. As a results of improving the system, the combined standard uncertainty has been improved $1.099{\times}10^{-3}$ to $2.332{\times}10^{-4}$. So calibration and measurement capability got 0.08 percent of the relative expanded uncertainty for maximum flow rate using the coverage factor(k=2).

• Smart Structures and Systems
• /
• v.22 no.4
• /
• pp.399-411
• /
• 2018

The difficulty in modeling complex nonlinear structures lies in the presence of significant sources of uncertainties mainly attributed to sudden changes in the structure's behavior caused by regular aging factors or extreme events. Quantifying these uncertainties and accurately representing them within the complex mathematical framework of Structural Health Monitoring (SHM) are significantly essential for system identification and damage detection purposes. This study highlights the importance of uncertainty quantification in SHM frameworks, and presents a comparative analysis between intrusive and non-intrusive techniques in quantifying uncertainties for SHM purposes through two different variations of the Kalman Filter (KF) method, the Ensemble Kalman filter (EnKF) and the Polynomial Chaos Kalman Filter (PCKF). The comparative analysis is based on a numerical example that consists of a four degrees-of-freedom (DOF) system, comprising Bouc-Wen hysteretic behavior and subjected to El-Centro earthquake excitation. The comparison is based on the ability of each technique to quantify the different sources of uncertainty for SHM purposes and to accurately approximate the system state and parameters when compared to the true state with the least computational burden. While the results show that both filters are able to locate the damage in space and time and to accurately estimate the system responses and unknown parameters, the computational cost of PCKF is shown to be less than that of EnKF for a similar level of numerical accuracy.

• Journal of Advanced Navigation Technology
• /
• v.22 no.6
• /
• pp.630-635
• /
• 2018

In this paper, we consider the stability condition for the linear discrete systems with time-varying delay and unstructured uncertainty. The considered system has time invariant system matrices for non-delayed and delayed state variables, but its delay time is time-varying within certain interval and it is subjected to nonlinear unstructured uncertainty which only gives information on uncertainty magnitude. In the many previous literatures, the time-varying delay and unstructured uncertainty can not be dealt in simultaneously but separately. In the paper, new stability conditions are derived for the case to which two factors are subjected together and compared with the existing results considering only one factor. The new stability conditions improving many previous results are proposed as very effective inequality equations without complex numerical algorithms such as LMI(Linear Matrix Inequality) or Lyapunov equation. By numerical examples, it is shown that the proposed conditions are able to include the many existing results and have better performances in the aspects of expandability and effectiveness.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.17 no.5
• /
• pp.95-104
• /
• 2022

This study aims to find out the influence of the intolerance of uncertainty on entrepreneurial intention by focusing on the start-up support system awareness. While most of existing studies focused on the positive psychological variables and individual environmental characteristics influencing on the entrepreneurial intention, this study has taken a look at the influence of the intolerance of uncertainty as the psychological variable to the entrepreneurial intention to negatively act on the entrepreneurial intention. As a result of the analysis, the intolerance of uncertainty under the control of entrepreneurship and gender is shown to have the negative (-) influence on the entrepreneurial intention and has high level of recognition on the start-up support system perception, and the start-up support system perception is confirmed to have positive (+) influence on the entrepreneurial intention. And, it also indicates that, with respect to the influence of the intolerance of uncertainty on the entrepreneurial intention, the start-up support system perception has the partial medium effect. Following this result of the study, it provides following indications: First the existing studies on the entrepreneurial intention had not covered the intolerance of uncertainty but it is confirmed as the psychological variable with negative influence on the entrepreneurial intention. Second, it is feasible for the preliminary start-up businesses may turn the fear on start-up failure into positive entrepreneurial intention with the start-up support system perception as the leading vehicle. And, third, based on the result, the government should enhance the start-up support system perception even more by seeking ways of efficient publicity to enable more preliminary start-up businesses to participate in diverse start-up support policies. Lastly, it discusses the limitations of this study as well as proposal for ensuing study plans.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.31 no.8
• /
• pp.107-114
• /
• 2003

The effect of uncertainties caused by measured parameters, which are propagated to the uncertainty of total-to-total efficiency, are analyzed from a turbine performance test. The degree of reaction is 0.373 at the mean radius on a tested 3-D axial type turbine, and the performance test is conducted at the low pressure and cold temperature status. The uncertainty of turbine inlet and exit total pressure shows the strong propagation effect to the uncertainty of total-to-total efficiency. This means that a high precision pressure measuring system is required to reduce the uncertainty propagated by the pressure. In the uncertainty portion of each measured parameters to the uncertainty of total- to-total efficiency, the uncertainty by torque is the highest and the uncertainty by RPM is the lowest. In case of the total pressure, the effect of the uncertainty by torque is increased with the increasing RPM. The uncertainty of total pressure at the turbine exit is more important than that at the turbine exit.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2018.05a
• /
• pp.155-155
• /
• 2018

Uncertainty in flood forecasting using a coupled meteorological and hydrological model is arisen from various sources, especially the uncertainty comes from the inaccuracy of Quantitative Precipitation Forecasts (QPFs). In order to improve the capability of flood forecast, the uncertainty estimation and mitigation are required to perform. This study is conducted to investigate and reduce such uncertainty. First, ensemble QPFs are generated by using Monte - Carlo simulation, then each ensemble member is forced as input for a hydrological model to obtain ensemble streamflow prediction. Likelihood measures are evaluated to identify feasible member. These members are retained to define upper and lower limits of the uncertainty interval and assess the uncertainty. To mitigate the uncertainty for very short lead time, a blending method, which merges the ensemble QPFs with radar-based rainfall prediction considering both qualitative and quantitative skills, is proposed. Finally, blending bias ratios, which are estimated from previous time step, are used to update the members over total lead time. The proposed method is verified for the two flood events in 2013 and 2016 in the Yeonguol and Soyang watersheds that are located in the Han River basin, South Korea. The uncertainty in flood forecasting using a coupled Local Data Assimilation and Prediction System (LDAPS) and Sejong University Rainfall - Runoff (SURR) model is investigated and then mitigated by blending the generated ensemble LDAPS members with radar-based rainfall prediction that uses McGill algorithm for precipitation nowcasting by Lagrangian extrapolation (MAPLE). The results show that the uncertainty of flood forecasting using the coupled model increases when the lead time is longer. The mitigation method indicates its effectiveness for mitigating the uncertainty with the increases of the percentage of feasible member (POFM) and the ratio of the number of observations that fall into the uncertainty interval (p-factor).