• The KSFM Journal of Fluid Machinery
• /
• v.9 no.4 s.37
• /
• pp.7-12
• /
• 2006

Flow meters in partly filled pipes are set up and run in Korea nowadays, but there are no standard calibration procedures for the flow meters in accordance with ' ISO/IEC 17025. The standard calibration procedure based on the standard calibration procedures for flow meter in closed conduits and the technical notes of flow meter's makers ' is developed. The measurement uncertainty of the calibration for the flow meter in partly filled pipes is evaluated.

• Proceedings of the KIEE Conference
• /
• 1998.07c
• /
• pp.884-886
• /
• 1998

This paper presents an LQG based robust TCSC supplementary controller design to suppress the SSR in the FACTS. This controller is designed to have the robust stability against the plant model uncertainty. The robust stability is guaranteed using the $\mu$ analysis. Time domain simulations using a nonlinear system model show that the proposed TCSC supplementary controller can suppress the SSR efficiently against the plant model uncertainty.

• Proceedings of the KSME Conference
• /
• 2004.04a
• /
• pp.733-738
• /
• 2004

This paper deals with feedback control of a hydraulic unit for direct yaw moment control, a method used to actively maintain the dynamic stability of an automobile. The uncertain parameters and complex structure naturally call for empirical modeling of the hydraulic unit, which readily results in a control-oriented model with high fidelity. The identified model is cross-validated against experimental data under various conditions, which helps to establish model uncertainty. Then, the $H_{\infty}$ optimization technique is employed to synthesize a controller with guaranteed robust stability and performance against the model uncertainty. The performance of the synthesized controller is verified using experimental results, which shows the viability of the proposed approach in a real-world application.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.38 no.1
• /
• pp.74-82
• /
• 2015

Two key challenges in statistical relational learning are uncertainty and complexity. Standard frameworks for handling uncertainty are probability and first-order logic respectively. A Markov logic network (MLN) is a first-order knowledge base with weights attached to each formula and is suitable for classification of dataset which have variables correlated with each other. But we need domain knowledge to construct first-order logics and a computational complexity problem arises when calculating weights of first-order logics. To overcome these problems we suggest a method to generate first-order logics and learn weights using association analysis in this study.

• Journal of Institute of Control, Robotics and Systems
• /
• v.9 no.7
• /
• pp.547-555
• /
• 2003

This paper presents an adaptive control against uncertainties in tail-controlled STT (Skid-to-Turn) missiles. We derive an analytic uncertainty model from a parametric affine missile model developed by the authors. Based on this analytic model, an adaptive feedback linearizing control law accompanied by a sliding mode control law is proposed. We provide analyses of stability and output tracking performance of the overall adaptive missile system. The performance and validity of the proposed adaptive control scheme are demonstrated by simulation.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.52 no.8
• /
• pp.462-470
• /
• 2003

This paper presents an algorithm of identifying parametric uncertainty by way of an interval model. For a given set of frequency response data from an uncertain linear SISO system of which the upper and the lower bounds of both magnitude and phase responses are represented, the proposed algorithm consists of two main parts: first, the nominal model is identified by using Least Square Estimation (LSE), and then an interval model is constructed by expanding the extremal properties of interval systems, so that tightly enclose the given envelopes within those of interval model. Two numerical examples are given to demonstrate and verify the developed algorithm. The identified interval model can be used for evaluating the worst case performance and stability margins against parametric uncertainty by using some extremal properties on interval systems.

• Journal of Electrical Engineering and Technology
• /
• v.12 no.3
• /
• pp.1166-1176
• /
• 2017

Estimation errors of the rotor speed and position in sensorless control systems of Permanent Magnet Synchronous Motors (PMSM) will lead to low efficiency and dynamic-performance degradation. In this paper, a parallel-type extended nonlinear observer incorporating the nominal parameters is constructed in the stator-fixed reference frame, with rotor position, speed, and the load torque simultaneously estimated. The stability of the extended nonlinear observer is analyzed using the indirect Lyapunov's method, and observer gains are selected according to the transfer functions of the speed and position estimators. Taking into account the parameter inaccuracies issue, explicit estimation error equations are derived based on the error dynamics of the closed-loop sensorless control system. An equivalent flux error is defined to represent the back Electromotive Force (EMF) error caused by the inaccurate motor parameters, and a compensation strategy is designed to suppress the estimation errors. The effectiveness of the proposed method has been validated through simulation and experimental results.

• Proceedings of the KIEE Conference
• /
• 1996.07b
• /
• pp.1037-1041
• /
• 1996

Input-output linearization technique in nonlinear control does not guarantee the robustness in the presence of parameter uncertainty or unmodeled dynamics, etc. However, it has been used as an important preliminary step in achieving additional control objectives, for instance, robustness to parameter uncertainty and disturbance attenuation. An indirect adaptive control scheme based on input-output linearization is proposed in this paper. The scheme consists of a Hopfield network for process parameter identification and an adaptive sliding mode controller based on input-output linearization, which steers the system response into a desired configuration. A numerical example is presented for the trajectory tracking of uncertain nonlinear dynamic systems with slowly time-varying parameters.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.57 no.8
• /
• pp.1429-1433
• /
• 2008

In this paper, we consider the design method of robust guaranteed cost controller for discrete-time singular systems with norm-bounded time-varying parameter uncertainty. In order to get the optimum(minimum) value of guaranteed cost, an optimization problem is given by linear matrix inequality (LMI) approach. The sufficient condition for the existence of controller and the upper bound of guaranteed cost function are proposed in terms of strict LMIs without decompositions of system matrices. Numerical examples are provided to show the validity of the presented method.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2015.05a
• /
• pp.226-226
• /
• 2015

The complex climate system regarding human actions is well represented through global climate models (GCMs). The output from GCMs provides useful information about the rate and magnitude of future climate change. Especially, the temperature variable is most reliable among other GCM outputs. However, hydrological variables (e.g. precipitation) from GCM outputs for future climate change contain too high uncertainty to use in practice. Therefore, we propose a method that simulates temperature variable with increasing in a certain level (e.g. 0.5oC or 1.0oC increase) as a global warming scenario from observed data. In addition, a hydrometeorological variable can be simulated employing block-wise sampling technique associated with the temperature simulation. The proposed method was tested for assessing the future change of the seasonal precipitation in South Korea under global warming scenario. The results illustrate that the proposed method is a good alternative to levy the variation of hydrological variables under global warming condition.