• International Journal of Aeronautical and Space Sciences
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• v.18 no.4
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• pp.623-640
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• 2017

The full nonlinear equations of an unmanned aerial vehicle ground taxiing mathematical dynamic model are built based on a type of unmanned aerial vehicle data in LMS Virtual.Lab Motion. The flexible landing gear model is considered to make the aircraft ground motion more accurate. The electric braking control system is established in MATLAB/Simulink and the experiment of it verifies that the electric braking model with the pressure sensor is fitted well with the actual braking mechanism and it ensures the braking response speediness. The direction rectification control law combining the differential brake and the rudder with 30% anti-skid brake is built to improve the directional stability. Two other rectifying control laws are demonstrated to compare with the designed control law to verify that the designed control is of high directional stability and high braking efficiency. The lateral displacement increases by 445.45% with poor rectification performance under the only rudder rectifying control relative to the designed control law. The braking distance rises by 36m and the braking frequency increases by 85.71% under the control law without anti-skid brake. Different landing conditions are simulated to verify the good robustness of the designed rectifying control.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.04a
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• pp.478-482
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• 1996

This paper presents a new approach to the design of adaptive control system using DSPs(TMS320C50) for robotic manipulators to achieve trajectorytracking angles. Digital signal processors are used in implementing real time adaptive control algorithms to provide motion for robotic manipulators. In the proposed scheme, adapation laws are derived from the improved second stability analysis based on the indirect adaptive control theory.l The proposed control scheme is simple in structure, fast in computation, an suitable for implementation of real-time control. Moreover, this scheme does not requre an accurate dynamic modeling, nor values of manipulator paramenters and payload Performance of the adaptive controller is illustrated by exeperimental results for a SCARA robot.

• Journal of the Institute of Convergence Signal Processing
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• v.19 no.2
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• pp.77-88
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• 2018

This paper proposes a RBFNN(Radial Basis Function Neural Network) based decentralized adaptive tracking control scheme using PSO(Particle Swarm Optimization) for an uncertain electrically driven robot system with input saturation. Practically, the magnitudes of input voltage and current signals are limited due to the saturation of actuators in robot systems. The proposed controller overcomes this input saturation and does not require any robot link and actuator model parameters. The fitness function used in the presented PSO scheme is expressed as a multi-objective function including the magnitudes of voltages and currents as well as the tracking errors. Using a PSO scheme, the control gains and the number of the RBFs are tuned automatically and thus the performance of the control system is improved. The stability of the total control system is guaranteed by the Lyapunov stability analysis. The validity and robustness of the proposed control scheme are verified through simulation results.

• Journal of the Korea Institute of Military Science and Technology
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• v.14 no.5
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• pp.747-752
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• 2011

Recently, skid steering methods have been increasingly applied to unmanned ground vehicles since they can provide a narrow turn that general steering methods like ackerman steering may not provide. However, dynamic behaviors of the skid steering vehicles with articulating arms which occur during a steering are very complicated and coupled. This makes it difficult to control vehicles and in severe case vehicles may loose stability. There are two methods to control unmanned ground vehicles. The first one is speed control method generally used with easiness and robustness in remote vehicle control. The next one is torque control allowing the vehicles to get better performance in several cases provided careful application is achieved. This paper addresses dynamic phenomena of skid steering vehicles during steering and compares with vehicle driving control methods between torque(traction force) control and speed control.

• Journal of Mechanical Science and Technology
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• v.20 no.12
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• pp.2218-2229
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• 2006

A conservative finite-volume numerical method for unstructured grids with the cell-centered method has been developed for computing flow and heat transfer by combining the attractive features of the existing pressure-based procedures with the advances made in unstructured grid techniques. This method uses an integral form of governing equations for arbitrary convex polyhedra. Care is taken in the discretization and solution procedure to avoid formulations that are cell-shape-specific. A collocated variable arrangement formulation is developed, i.e. all dependent variables such as pressure and velocity are stored at cell centers. For both convective and diffusive fluxes the forms superior to both accuracy and stability are particularly adopted and formulated through a systematic study on the existing approximation ones. Gradients required for the evaluation of diffusion fluxes and for second-order-accurate convective operators are computed by using a linear reconstruction based on the divergence theorem. Momentum interpolation is used to prevent the pressure checkerboarding and a segregated solution strategy is adopted to minimize the storage requirements with the pressure-velocity coupling by the SIMPLE algorithm. An algebraic solver using iterative preconditioned conjugate gradient method is used for the solution of linearized equations. The flow analysis code (PowerCFD) developed by the present method is evaluated for its application to several 2-D structured-mesh benchmark problems using a variety of unstructured quadrilateral and triangular meshes. The present flow analysis code by using unstructured grids with the cell-centered method clearly demonstrate the same accuracy and robustness as that for a typical structured mesh.

• The Journal of the Convergence on Culture Technology
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• v.10 no.5
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• pp.775-780
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• 2024

In this study, a simultaneous analysis method was developed using reversed-phase high-performance liquid chromatography(RP-HPLC) for the main components of motion sickness medication: Dimenhydrinate, Caffeine, Methyl Paraben, and Propyl Paraben. The analysis was conducted after pretreating the four components, utilizing a C18 column with Acetonitrile and H₂O as the mobile phase. UV detection was carried out at a wavelength of 254nm. As a result of the experiment, the values of Resolution(Rs) was well over Rs > 1.5, which indicates that the separation of the four components was efficient. Additionally, the symmetry factor of the components was 0.989, 1.120, 1.256, and 1.280, respectively, showing their symmetrical stability. In the stability assessment, the calibration curves for the four components showed excellent linearity with R² > 0.9991 to 0.9998. Furthermore, the limit of detection(LOD) ranged between 0.017 to 3.060㎍/ml, while the limit of quantification(LOQ) ranged between 0.050 to 9.180 ㎍/ml. The recovery rates range from 98.28% to 101.71%, and repeatability showed precision within the range of 0.447 to 0.550, and robustness confirmed values with %RSD < 2. The quantitative analysis results of this study demonstrated the effectiveness of a simultaneous analysis method for motion sickness medication components.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.33 no.5
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• pp.18-27
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• 2005

Airfoils with improved longitudinal static stability were designed for a WIG craft through aerodynamic design optimization. The response surface method is coupled with NURBS-based shape functions and Navier-Stokes flow analysis. The procedure runs in the network-distributed design framework of commercial-code based automated design capability to enhance computational efficiency and robustness.Lift maximization design maintaining similar static margin to a DHMTU airfoil successfully produced a new airfoil shape characterized by pronounced front-loading and the well-known reflexed aft-camber line. Another airfoil design of lower variation in pitching moment during take-off showed weakened front-loaded characteristics and hence decreased lift slightly. Investigations using the present design methodology on an existing optimization result based on potential flow analysis and NACA-type geometry generation demonstrated significance of carrying various geometry generations and more realistic flow analysis with optimization.

• Journal of Communications and Networks
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• v.9 no.1
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• pp.18-27
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• 2007

The conventional normalized least mean square (NLMS) algorithm is the most widely used for adaptive identification within a non-stationary input context. The convergence of the NLMS algorithm is independent of environmental changes. However, its steady state performance is impaired during input sequences with low dynamics. In this paper, we propose a new NLMS algorithm which is, in the steady state, insensitive to the time variations of the input dynamics. The square soot (SR)-NLMS algorithm is based on a normalization of the LMS adaptive filter input by the Euclidean norm of the tap-input. The tap-input power of the SR-NLMS adaptive filter is then equal to one even during sequences with low dynamics. Therefore, the amplification of the observation noise power by the tap-input power is cancelled in the misadjustment time evolution. The harmful effect of the low dynamics input sequences, on the steady state performance of the LMS adaptive filter are then reduced. In addition, the square root normalized input is more stationary than the base input. Therefore, the robustness of LMS adaptive filter with respect to the input non stationarity is enhanced. A performance analysis of the first- and the second-order statistic behavior of the proposed SR-NLMS adaptive filter is carried out. In particular, an analytical expression of the step size ensuring stability and mean convergence is derived. In addition, the results of an experimental study demonstrating the good performance of the SR-NLMS algorithm are given. A comparison of these results with those obtained from a standard NLMS algorithm, is performed. It is shown that, within a non-stationary input context, the SR-NLMS algorithm exhibits better performance than the NLMS algorithm.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.10
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• pp.1879-1885
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• 2017

This paper discusses the trajectory tracking system of unmanned ground vehicles based on predictive control. Because the unmanned ground vehicles can not satisfactorily complete the path tracking task, highly efficient and stable trajectory control system is necessary for unmanned ground vehicle to be realized intelligent and practical. According to the characteristics of unmanned vehicle, this paper built the kinematics tracking models firstly. Then studied algorithm solution with the tools of the optimal stability analysis method and proposed a tracking control method based on the model predictive control. The controller used a kinematics-based prediction model to calculate the predictive error. This controller helps the unmanned vehicle drive along the target trajectory quickly and accurately. The designed control strategy has the true robustness, simplicity as well as generality for kinematics model of the unmanned vehicle. Furthermore, the computer Simulink/Carsim results verified the validity of the proposed control method.

• Journal of Computing Science and Engineering
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• v.7 no.2
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• pp.99-111
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• 2013

Approximating a divergence between two probability distributions from their samples is a fundamental challenge in statistics, information theory, and machine learning. A divergence approximator can be used for various purposes, such as two-sample homogeneity testing, change-point detection, and class-balance estimation. Furthermore, an approximator of a divergence between the joint distribution and the product of marginals can be used for independence testing, which has a wide range of applications, including feature selection and extraction, clustering, object matching, independent component analysis, and causal direction estimation. In this paper, we review recent advances in divergence approximation. Our emphasis is that directly approximating the divergence without estimating probability distributions is more sensible than a naive two-step approach of first estimating probability distributions and then approximating the divergence. Furthermore, despite the overwhelming popularity of the Kullback-Leibler divergence as a divergence measure, we argue that alternatives such as the Pearson divergence, the relative Pearson divergence, and the $L^2$-distance are more useful in practice because of their computationally efficient approximability, high numerical stability, and superior robustness against outliers.