• Proceedings of the KIEE Conference
• /
• 2006.04b
• /
• pp.155-157
• /
• 2006

The paper is proposed maximum torque control of SynRM drive using adaptive learning mechanism-fuzzy neural network(ALM-FNN) controller and artificial neural network(ANN). The control method is applicable over the entire speed range and considered the limits of the inverter's current and voltage rated value. For each control mode, a condition that determines the optimal d-axis current $i_d$ for maximum torque operation is derived. The proposed control algorithm is applied to SynRM drive system controlled ALM-FNN and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper is proposed the analysis results to verify the effectiveness of the ALM-FNN and ANN controller.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.55 no.3
• /
• pp.110-114
• /
• 2006

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. In this paper maximum torque control of IPMSM drive using artificial intelligent(AI) controller is proposed. The control method is applicable over the entire speed range and considered the limits of the inverter's current and voltage rated value. For each control mode, a condition that determines the optimal d-axis current $i_d$ for maximum torque operation is derived. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using AI controller. This paper is proposed speed control of IPMSM using adaptive learning mechanism fuzzy neural network(ALM-FNN) and estimation of speed using artificial neural network(ANN) controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The proposed control algorithm is applied to IPMSM drive system controlled ALM-FNN and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper is proposed the experimental results to verify the effectiveness of AI controller.

• Proceedings of the KIEE Conference
• /
• 2005.10c
• /
• pp.198-201
• /
• 2005

The paper is proposed maximum torque control of IPMSM drive using adaptive learning mechanism-fuzzy neural network (ALM-FNN) controller and artificial neural network(ANN). The control method is applicable over the entire speed range and considered the limits of the inverter's current and voltage rated value. For each control mode, a condition that determines the optimal d-axis current $^i_d$ for maximum torque operation is derived. The proposed control algorithm is applied to IPMSM drive system controlled ALM-FNN and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper is proposed the analysis results to verily the effectiveness of the ALM-FNN and ANN controller.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
• /
• 2009.05a
• /
• pp.408-411
• /
• 2009

In this paper, conventional PI, fuzzy neural network(FNN) and adaptive teaming mechanism(ALM)-FNN for rotor field oriented controlled(RFOC) induction motor are studied comparatively. The widely used control theory based design of PI family controllers fails to perform satisfactorily under parameter variation nonlinear or load disturbance. In high performance applications, it is useful to automatically extract the complex relation that represent the drive behaviour. The use of learning through example algorithms can be a powerful tool for automatic modelling variable speed drives. They can automatically extract a functional relationship representative of the drive behavior. These methods present some advantages over the classical ones since they do not rely on the precise knowledge of mathematical models and parameters. Comparative study of PI, FNN and ALM-FNN are carried out from various aspects which is dynamic performance, steady-state accuracy, parameter robustness and complementation etc. To have a clear view of the three techniques, a RFOC system based on a three level neutral point clamped inverter-fed induction motor drive is established in this paper. Each of the three control technique: PI, FNN and ALM-FNN, are used in the outer loops for rotor speed. The merit and drawbacks of each method are summarized in the conclusion part, which may a guideline for industry application.

• Journal of the Korean Society of Hazard Mitigation
• /
• v.10 no.3
• /
• pp.31-38
• /
• 2010

In this study, it is suggested that it has to reliability-based design methodology with respect to bridge structure-rail longitudinal interaction and bridge structure-vehicle interaction. For the structural analysis, commercial package, ABAQUS, are used for a three-dimensional finite element analysis. The optimization process utilizes a well-known optimizer, ADS(Automated Design Synthesis). Optimization technique is utilized the ALM-BFGS method for global area search and Golden Section Method for 1-D search. In general, ALM-BFGS method don't need the 1-D search, and that algorithm converge a 0.1~0.2 of Push-Off factor. But in this study, value of Push-Off factor is used 90, therefore 1-D search should be needed for effective convergency. That algorithm contains the "heuristic decision method". As a result of optimum design of 2-main steel girder birdge with 5${\times}$(1@50m), design methodology suggested in this study was demonstrated more economic and efficient than existing design and LCC optimization not considering bridge-rail longitudinal interaction and bridge-vehicle interaction.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.20 no.2
• /
• pp.105-110
• /
• 2002

For the accuracy assessment of DTM obtained by Airborne Laser Mapping Systems (ALMS), we selected 500 m spaced test points and analyze the accuracy of the DTM by various methods including GPS survey. To estimate the height at the point in between the test points of the DTM we produced a TIN, and to analyze the accuracy we use a GPS coverage map overlaid with the TIN. Compared with the existing method, DTM by ALMS is shown to be relatively accurate, and therefore, ALMS is applicable to 1/5000 digital terrain mapping.

• 한국전산유체공학회:학술대회논문집
• /
• 2009.11a
• /
• pp.65-70
• /
• 2009

In this paper a hybrid grid generation program for general 2-D region is introduced. The program is developed by using JAVA programming language, and it can be used either as an application program on a local computer or as an applet in the network environment. The hybrid grid system for a 2-D problem means a combination of triangular cells and quadrilateral cells, and it can offer both of the high flexibility of triangular cells and the high accuracy and efficiency of structured-type quadrilateral cells. To accommodate a quadrilateral-cell region and a triangular-cell region into one computational domain, it is importance to take good care of the interface between two different regions so that overall good grid quality can be maintained. In this research advancing layer method(ALM) augmented by elliptic smoothing method is used for the quadrilateral-cell region and advancing front method(AFM) is used for the triangular-cell region. A special treatment technique for the interface between those two regions is also developed. The interface treatment technique is basically to prevent the propagation of small cell size due to ALM method into the triangular region and maintain the smooth transition of cell-size scale between two different regions. By applying current technique high-quality hybrid grids for general 2-D regions can be easily generated, and typical grid generation results and flow solutions are demonstrated.

• Transactions of the Korean Society of Mechanical Engineers B
• /
• v.24 no.5
• /
• pp.640-651
• /
• 2000

For the heat and fluid flow analyses of a parallel flow heat exchanger, an improved model considering the effect of flat tube with micro-channels is proposed. The effect of flow distribution on the thermal performance of a heat exchanger is numerically investigated. The flow distribution is examined by varying geometrical parameters, i.e., the position of the separators and the inlet/outlet, and the aspect ratio of micro-channels of the heat exchanger. The flow nonuniformities along the paths of the heat exchanger are proposed and observed to evaluate the thermal performance of the heat exchanger. The optimization using ALM method has been accomplished by minimizing the flow nonuniformity. It is found that the heat transfer rate of the optimized model is increased by 6.0% of that of the reference heat exchanger model, and the pressure drop by 0.4%

• Journal of Mechanical Science and Technology
• /
• v.15 no.8
• /
• pp.1143-1155
• /
• 2001

In order to reduce the expensive CPU time for design sensitivity analysis in dynamic response optimization, this study introduces the design sensitivities approximated within estimated confidence radius in dynamic response optimization with ALM method. The confidence radius is estimated by the linear approximation with Hessian of quasi-Newton formula and qualifies the approximate gradient to be validly used during optimization process. In this study, if the design changes between consecutive iterations are within the estimated confidence radius, then the approximate gradients are accepted. Otherwise, the exact gradients are used such as analytical or finite differenced gradients. This hybrid design sensitivity analysis method is embedded in an in-house ALM based dynamic response optimizer, which solves three typical dynamic response optimization problems and one practical design problem for a tracked vehicle suspension system. The optimization results are compared with those of the conventional method that uses only exact gradients throughout optimization process. These comparisons show that the hybrid method is more efficient than the conventional method. Especially, in the tracked vehicle suspension system design, the proposed method yields 14 percent reduction of the total CPU time and the number of analyses than the conventional method, while giving similar optimum values.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
• /
• 2005.11a
• /
• pp.309-314
• /
• 2005

The paper is proposed maximum torque control of SynRM drive using learming mechanism-fuzzy neural network(LM-FNN) controller and artificial neural network(ANN). The control method is applicable over the entire speed range and considered the limits of the inverter's current and voltage rated value. For each control mode, a condition that determines the optimal d-axis current $^i{_d}$ for maximum torque operation is derived. The proposed control algorithm is applied to SynRM drive system controlled LM-FNN and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper is proposed the analysis results to verify the effectiveness of the LM-FNN and ANN controller.