• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.5
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• pp.560-570
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• 1999

This paper formulates the suboptimal robust Kalman filtering problem into two coupled Linear Matrix Inequality (LMI) problems by applying Lyapunov theory to the augmented system which is composed of the state equation in the uncertain linear system and the estimation error dynamics. This formulations not only provide the sufficient conditions for the existence of the desired filter, but also construct the suboptimal robust Kalman filter. The proposed filter can guarantee the optimized upper bound of the estimation error variance for uncertain systems with parametric uncertainties in both the state and measurement matrices. In addition, this paper shows how the problem of finding the minimizing solution subject to Quadratic Matrix Inequality (QMI), which cannot be easily transformed into LMI using the usual Schur complement formula, can be successfully modified into a generic LMI problem.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.11
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• pp.101-107
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• 2000

A design of the global optimal sliding mode control is presented to control the second order uncertain time varying system with torque limit. With specified ranges of parametric uncertainties and torque limit, the minimum arrival time to reference inputs can be calculated. The proposed control scheme is applied to the motor system carrying loads. The merit of the proposed control scheme is that the arriving time at the reference input, which is the revolution angle, and the maximum allowable acceleration are expressed in a closed form solution. The superior performance of the proposed control scheme is validated by the computer simulation and experiments comparing with other sliding mode controllers.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.20 no.10
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• pp.900-906
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• 2010

This note proposes vibration-free motor control through modified LQG/LTR methodology. A conventional LQG/LTR method is a design tool in the frequency domain. However, unlike the conventional one, the proposed one is a time response based design method. This feature is firstly designed by parameterized settling time control gain through the target loop design procedure and the feature is secondly realized by loop transfer recovery. In order to show convergence to the target loop transfer functions, asymptotic behaviors of the open and the closed loop transfer functions are shown. At the conclusion, it is verified that the proposed method is robustly stable to parametric uncertainties through ${\mu}$-plot.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.95.3-95
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• 2001

This paper investigates the simultaneous use of mixed H2/H_inf and mu-synthesis design methodology to design a robust controller for flexible link. We adopt four steps to design control system as follows: Step 1 : Generally, there are differences between the nominal and real model, so we consider the plant as a combination of parametric model uncertainty and unstructured uncertainty represents real structural uncertainties associated with the damping ratios of the flexible modes retained in the nominal model without payload. denotes the uncertainty which is due to the payload added at the tip. Step 2 : We adopt the mixed H2/H_inf theory to design a feedback controller K(s) by using the model uncertainty ...

• Journal of the Korean Institute of Intelligent Systems
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• v.5 no.4
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• pp.101-110
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• 1995

In this paper, a DC servo motor position control system based on acceleration control is proposed. The proposed control system consists of an acceleration controller and an auto-tuqing fuzzy PID controller. The auto-tuning fuzzy PID controller provides corrections for an acceleration reference to remove the effect of parametric uncertainties. And it comprises of the expert system which performs the automatic tuning of the PID controller parameters and the conventional PID controller. Expermental results demonstrate strate thi~tth e proposed overall control system has robust properties and good control performances with regard to unmeasurable disturbances and parameter variations. Therefore, the proposed control scheme enhances the applicability of an acceleration control approach and especially performs accurate position control under such an operating environment that model uncertainties exist and/or load, etc. change significantly.

• Journal of the Korea Concrete Institute
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• v.28 no.6
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• pp.723-731
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• 2016

In pretensioned concrete members, a certain bond length from the end of the member is required to secure the effective prestress in the strands, which is defined as the transfer length. However, due to the complex bond mechanism between strands and concrete, most transfer length models based on the deterministic approach have uncertainties and do not provide accurate estimations. Therefore, in this study, Adaptive Neuro-Fuzzy Inference System (ANFIS), a Neuro-Fuzzy System, is introduced to reduce the uncertainties and to estimate the transfer length more accurately in pretensioned concrete member. A total of 253 transfer length test results have been collected from literatures to train ANFIS, and the trained ANFIS algorithm estimated the transfer length very accurately. In addition, a design equation was proposed to calculate the transfer length based on parametric studies and dimensional analyses. Consequently, the proposed equation provided accurate results on the transfer length which are comparable to the ANFIS analysis results.

• Journal of Soil and Groundwater Environment
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• v.17 no.5
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• pp.56-67
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• 2012

In the present study, an enhanced subsurface prediction algorithm based on a non-parametric geostatistical model and a history matching technique through Gibbs sampler is developed and the iterative prediction improvement procedure is proposed. The developed model is applied to a simple two-dimensional synthetic case where domain is composed of three different hydrogeologic media with $500m{\times}40m$ scale. In the application, it is assumed that there are 4 independent pumping tests performed at different vertical interval and the history curves are acquired through numerical modeling. With two hypothetical borehole information and pumping test data, the proposed prediction model is applied iteratively and continuous improvements of the predictions with reduced uncertainties of the media distribution are observed. From the results and the qualitative/quantitative analysis, it is concluded that the proposed model is good for the subsurface prediction improvements where the history data is available as a supportive information. Once the proposed model be a matured technique, it is believed that the model can be applied to many groundwater, geothermal, gas and oil problems with conventional fluid flow simulators. However, the overall development is still in its preliminary step and further considerations needs to be incorporated to be a viable and practical prediction technique including multi-dimensional verifications, global optimization, etc. which have not been resolved in the present study.

• Journal of the Earthquake Engineering Society of Korea
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• v.2 no.1
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• pp.1-10
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• 1998

In the dynamic soil-structure interaction(SSI) analysis, numerous uncertain parameters are involved. They include the uncertainties in the definition of input motions, modeling of soil-structure interaction systems. analysis techniques, etc. This paper presents the results of parametric studies of the seismic responses of a reactor containment structure built on the viscoelastic layered soil. Among the numerous parameter, this study concentrates on the effects of definition point of the input motion, embedment of structure to the base soil, thickness of the top soil layer, and rigidity of the base soil. The substructure method using frequency independent impedances is adopted. The method is based on the mode superposition method in time domain using the composite modal damping values of th SSI system computed from the ratio of dissipated energy to the strain energy for each model. From the study results, the sensitivity of each parameter on the earthquake responses has been suggested for the practical application of the substructure method of SSI analysis.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.1
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• pp.73-80
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• 2018

In motorway traffic flow control, the conventional way based on real-time response has been changed into advanced way based on proactive response. Future traffic conditions over multiple time intervals are crucial input data for advanced motorway traffic flow control. It is necessary to overcome the uncertainty of the future state in order for forecasting multiple-period traffic volumes, as the number of uncertainty concurrently increase when the forecasting horizon expands. In this vein, multi-interval forecasting of traffic volumes requires a viable approach to conquer future uncertainties successfully. In this paper, a forecasting model is proposed which effectively addresses the uncertainties of future state based on the behaviors of temporal evolution of traffic volume states that intrinsically exits in the big past data. The model selects the past states from the big past data based on the state evolution of current traffic volumes, and then the selected past states are employed for estimating future states. The model was also designed to be suitable for data management systems in practice. Test results demonstrated that the model can effectively overcome the uncertainties over multiple time periods and can generate very reliable predictions in term of prediction accuracy. Hence, it is indicated that the model can be mounted and utilized on advanced data management systems.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.316-321
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• 2003

This paper presents a design method of the wavelet neural network based controller using direct adaptive control method to deal with a stable intelligent control of chaotic systems. The various uncertainties, such as mechanical parametric variation, external disturbance, and unstructured uncertainty influence the control performance. However, the conventional control methods such as optimal control, adaptive control and robust control may not be feasible when an explicit, faithful mathematical model cannot be constructed. Therefore, an intelligent control system that is an on-line trained WNN controller based on direct adaptive control method with adaptive learning rates is proposed to control chaotic nonlinear systems whose mathematical models are not available. The adaptive learning rates are derived in the sense of discrete-type Lyapunov stability theorem, so that the convergence of the tracking error can be guaranteed in the closed-loop system. In the whole design process, the strict constrained conditions and prior knowledge of the controlled plant are not necessary due to the powerful learning ability of the proposed intelligent control system. The gradient-descent method is used for training a wavelet neural network controller of chaotic systems. Finally, the effectiveness and feasibility of the proposed control method is demonstrated with application to the chaotic systems.