• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.515-515
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• 2000

The performance of dynamic path following of a wheeled mobile robot with nonholonomic constraints has some drawbacks such as the influence of the initial state. The drawbacks can be overcome by the temporary path generator and modified output. But with the previous input-output linearization method using them, it is difficult to tune the gains, and if there are some modeling errors, the low gain can make the system unstable. And if a high gain is used to overcome the model uncertainties, the control inputs are apt to be large so the system can be unstable. In this paper. an H$_{\infty}$ controller is designed to guarantee robustness to model parameter uncertainties and to consider the magnitude of control inputs. And the solution to Hamilton Jacobi (HJ) inequality, which is essential to H$_{\infty}$ control design, is obtained by nonlinear matrix inequality (NLMI).

• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.441-445
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• 1997

In this paper, we suggest a design scheme of receding horizon predictive control(RHPC) for boiler-turbine systems whose dynamics are given in nonlinear equations. RHPC is designed for linear state space models which are obtained at a nominal operating point of the boiler-turbine system. In this consideration, the boiler is operated in a sliding pressure mode, in which the reference value of drum pressure is changing according to the electrical power generation. The reference values of the system outputs are prefiltered before they are fed to the RHPC in order to compensate the linearization errors. Simulation results show that the proposed controller provides acceptable performances in both of the cases of 'steep and small changes' and 'slow and large changes' of power demand and yields the effect of modest coordination of conventional PID schemes such as boiler-following and turbine-following control.

• 한국전산유체공학회:학술대회논문집
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• 2003.08a
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• pp.18-24
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• 2003

In the present study, solution algorithms for the connotation of unsteady flows on an unstructured mesh me presented Dual time stepping is incorporated to achieve the 2-nd order temporal accuracy while reducing the linearization and the factorization errors associated with a linear solver. Hence, any time step can be used by only considering physical phenomena. Gauss-Seidel scheme is used to solve linear system of equations. Rigid motion and suing analogy method for moving mesh are all considered and compared. Special treatments of suing analogy for high aspect ratio cells are presented. Finally, numerical results for oscillating ing are compared with experimental data.

• Magazine of the Korean Society of Agricultural Engineers
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• v.35 no.2
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• pp.57-64
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• 1993

A modified kinematic wave model of the overland flow in vegetated filter strips was developed. The model can predict both flow depth and hydraulic radius of the flow. Existing models can predict only mean flow depth. By using the hydraulic radius, erosion, deposition and flow's transport capacity can be more rationally computed. Spacing hydraulic radius was used to compute flow's hydraulic radius. Numerical solution of the model was accomplished by using both a second-order nonlinear scheme and a linear solution scheme. The nonlinear portion of the model ensures convergence and the linear portion of the model provides rapid computations. This second-order nonlinear scheme minimizes numerical computation errors that may be caused by linearization of a nonlinear model. This model can also be applied to golf courses, parks, no-till fields to route runoff and production and attenuation of many nonpoint source pollutants.

• The Journal of Korea Robotics Society
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• v.3 no.3
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• pp.176-185
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• 2008

In this paper, new filtering method for sensor registration is provided to estimate and correct error of registration parameters in multiple sensor environments. Sensor registration is based on filtering method to estimate registration parameters in multiple sensor environments. Accuracy of sensor registration can increase performance of data fusion method selected. Due to various error sources, the sensor registration has registration errors recognized as multiple objects even though multiple sensors are tracking one object. In order to estimate the error parameter, new nonlinear information filtering method is developed using minimum mean square error estimation. Instead of linearization of nonlinear function like an extended Kalman filter, information estimation through unscented prediction is used. The proposed method enables to reduce estimation error without a computation of the Jacobian matrix in case that measurement dimension is large. A computer simulation is carried out to evaluate the proposed filtering method with an extended Kalman filter.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2331-2334
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• 2003

The basic difference between the EKF(Extended Kalman Filter) and UKF(Unscented Kalman Filter) stems from the manner in which Gaussian random variables(GRV) are represented for propagating through system dynamics. In the EKF, the state distribution is approximated by a GRV, which is then propagated analytically through the first-order linearization of the nonlinear system. This can possibly introduce large errors in the true posterior mean and covariance of the transformed GRV, which may lead to sub-optimal performance and sometimes divergence of the filter. However, the UKF addresses this problem by using a deterministic sampling approach. The state distribution is also approximated by a GRV, but is now represented using a minimal set of carefully chosen sample points. These sample points completely capture the true mean and covariance of the GRV, and UKF captures the posterior mean and covariance accurately up to the 2nd order(Taylor series expansion) for any nonlinearity. This paper utilizes the UKF to determine spacecraft orbit when only magnetometer is available. Several catastrophic failures of spacecraft in orbit have been attributed to failures of the spacecraft mission. Recently studies on contingency-major sensor failure cases- have been performed. For mission success, contingency design or plan should be implemented in case of a major sensor failure. Therefore the algorithm presented in this paper can be used for a spacecraft without GPS or contingency design in case of GPS failure.

• Steel and Composite Structures
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• v.27 no.1
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• pp.13-25
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• 2018

The main objective of this study is to develop a dual approach for geometrically nonlinear finite element analysis of plane truss structures. The geometric nonlinearity is considered using the Total Lagrangian formulation. The nonlinear solution is obtained by introducing and minimizing an objective function subjected to displacement-type constraints. The proposed method can fully trace the whole equilibrium path of geometrically nonlinear plane truss structures not only before the limit point but also after it. No stiffness matrix is used in the main approach and the solution is acquired only based on the direct classical stress-strain formulations. As a result, produced errors caused by linearization and approximation of the main equilibrium equation will be eliminated. The suggested algorithm can predict both pre- and post-buckling behavior of the steel plane truss structures as well as any arbitrary point of equilibrium path. In addition, an equilibrium path with multiple limit points and snap-back phenomenon can be followed in this approach. To demonstrate the accuracy, efficiency and robustness of the proposed procedure, numerical results of the suggested approach are compared with theoretical solution, modified arc-length method, and those of reported in the literature.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.32 no.4
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• pp.24-33
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• 2004

In this paper, the helicopter flight control system using online adaptive neural networks which have the universal function approximation property is considered. It is not compensation for modeling errors but approximation two functions required for feedback linearization control action from input/output of the system. To guarantee the tracking performance and the stability of the closed loop system replaced two nonlinear functions by two neural networks, weight update laws are provided by Lyapunov function and the simulation results in low speed flight mode verified the performance of the control system with the neural networks.

• Bulletin of the Korean Space Science Society
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• 2008.10a
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• pp.34.2-34.2
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• 2008

The batch least square filter is widely used for ground estimations. However, in orbit determination (OD) under inaccurate initial conditions and few measurement data the performance by the batch least square filter can lead an unstable results. To complement weak part of the batch filter, the batch unscented transformation without any linearization process is developed by ACL (Astrodynamics and Control Laboratory) in YONSEI University. In this paper, the batch unscented transformation is introduced and applied to satellite orbit determination using Satellite Laser Ranging (SLR) data. Only range of the satellite measured from ground tracking stations is used for measurement data. The results of simulation test are compared with those of the weighted batch least square filter for various initial states errors (position and velocity). Simulation results show that the batch unscented transformation is comparable or slightly superior to batch least square filter in the orbit determination.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.649-652
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• 1999

In this paper, the design method of target tracking systems using the extended $H^{\infty}$ filter(EHF) is proposed. Usually, a Cartesian coordinate frame is tell suited to describe the target dynamics. However, the measurements made in radar-centered polar coordinates are expressed as nonlinear equations in Cartesian coordinates. Thus the tacking problem is concerned with the nonlinear estimation. The extended $H^{\infty}$ filter is able to deal with the problems arising in the target tacking systems such as the parameter uncertainty included inevitably in modeling physical systems mathematically, the unavailableness of the stochastic information about exogenous disturbances, and errors due to the linearization of measurement equations. We show the proposed filter is robuster than the extended Kalman filter(EKF) through a simple target tracking example.