• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2005.11a
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• pp.963-966
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• 2005

Model updating method is known to the area to correct finite element models by the results of the experimental modal analysis. Most common methods in model updating depend on a parametric model of the structure. In this case, the number of parameters is normally smaller than that of modal data obtained from an experiment. In order to overcome this limitation, many researchers are trying to get modal data as many as possible to date. 1 want to name this method multiple modified-system generation method. These Methods consist of direct system modification method and feedback controller method. The direct system modification Is to add a mass or stiffness on the original structure or perturb the boundary conditions. The feedback controller method is to make the closed food system with sensor and actuator so as to get the closed loop modal data. In this paper, we need to focus on the feedback controller method because of its simplicity. Several methods related the feedback controller methods are virtual passive controller (VPC) sensitivity enhancement controller (SEC) and mode decoupling controller (MDC). Among them, we will apply MDC to the model updating problem. MDC has various advantages compared with other controllers, such as VPC and SEC. To begin with, only the target mode can be changed without changing modal property of non-target modes. In addition, it is possible to fix any modes if the number of sensors is equal to that of the system modes. Finally, the required control power to achieve desired change of target mode is always lower than those of other methods such as VPC. However, MDC can make the closed loop system unstable when using incomplete modal data. So we need to take action to avoid undesirable instability from incomplete modal data. In this paper, we address the method to design the unique and robust MDD obtained from incomplete modal data. The associated simulation will be Incorporated to demonstrate the usefulness of this method.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.11a
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• pp.282-286
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• 2000

In this paper, magneto-rheological(MR) damper is studied for vibration control of large infra structures under earthquake. Generally, active control devices need a large control force and a high power supply system to reduce the vibration effectively. Large and miss tuned control force may induce the dangerous situation such that the generated large control force acts to amplify the structural vibration. Recently, to overcome the weaknesses of the active control, the semi-active control method is suggested by many researchers. Semi-active control uses the passive control device of which the characteristics can be modified. Control force of the semi-active device is not generated from the actuator with power supply. It is generated as a dynamic reaction force of the device same as in the passive control case, so the control system is inherently stable and robust. Unlike the case of passive control, control force of semi-active control is adjusted depending on the measured response of the structure, so the vibration can be reduced more effectively against various unknown environmental loads. Magneto-rheological(MR) damper is one of the semi-active devices. Dynamic characteristics of the MR material can be changed by applying the magnetic fields. So the control of MR damper needs only small power. Response time of MR to the input voltage is very short, so the high performance control is possible. MR damper has a high force capacity so it is adequate to the vibration control of large infra structure. Because MR damper has a nonlinear property, normal control method used in active control may not be effective. Clipped optimal control, modified bang-bang control etc. have been suggested to MR damper by many researchers. In this study, sliding mode fuzzy control(SMFC) is applied to MR damper. Genetic algorithm is used for the controller tuning. To verify the applicability of MR damper and suggested algorithm, numerical simulation on the aseismic control is carried out. Simulation model is three-story building structure, which was used in the paper of Dyke, et al. The control performance is compared with clipped optimal control. The present results indicate that the SMFC algorithm can reduce the earthquake-induced vibration very effectively.

• Structural Engineering and Mechanics
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• v.85 no.4
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• pp.469-484
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• 2023

A deep recursive bidirectional Cuda Deep Neural Network Long Short Term Memory (Bi-CuDNNLSTM) layer is recruited in this paper to predict the entire force time histories, and the corresponding hysteresis and backbone curves of reinforced concrete (RC) bridge piers using experimental fast and slow cyclic tests. The proposed stacked Bi-CuDNNLSTM layers involve multiple uncertain input variables, including horizontal actuator displacements, vertical actuators axial loads, the effective height of the bridge pier, the moment of inertia, and mass. The functional application programming interface in the Keras Python library is utilized to develop a deep learning model considering all the above various input attributes. To have a robust and reliable prediction, the dataset for both the fast and slow cyclic tests is split into three mutually exclusive subsets of training, validation, and testing (unseen). The whole datasets include 17 RC bridge piers tested experimentally ten for fast and seven for slow cyclic tests. The results bring to light that the mean absolute error, as a loss function, is monotonically decreased to zero for both the training and validation datasets after 5000 epochs, and a high level of correlation is observed between the predicted and the experimentally measured values of the force time histories for all the datasets, more than 90%. It can be concluded that the maximum mean of the normalized error, obtained through Box-Whisker plot and Gaussian distribution of normalized error, associated with unseen data is about 10% and 3% for the fast and slow cyclic tests, respectively. In recapitulation, it brings to an end that the stacked Bi-CuDNNLSTM layer implemented in this study has a myriad of benefits in reducing the time and experimental costs for conducting new fast and slow cyclic tests in the future and results in a fast and accurate insight into hysteretic behavior of bridge piers.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.3
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• pp.1255-1262
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• 2012

Look into the nuclear power plant of Wolsong currently, it is controlled in order to required operating pressure with PI controller. PI controller has a simple structure and satisfy design requirements to gain setting. However, It is difficult to control without changing the gain from produce changes in parameters such as loss of the valves and the pipes. To solve these problems, the dynamic change of the PI controller gain, or to compensate for the PI controller output is desirable to configure the controller. The aim of this research and development in the parameter variations can be controlled to a stable controller design which is reduced an error and a vibration. Proposed PI/NN control techniques is the PI controller and the neural network controller that combines a parallel and the neural network controller part is compensated output of the controller for changes in the parameters were designed to be robust. To directly evaluate the controller performance can be difficult to test in real processes to reflect the characteristics of the process. Therefore, we develope the simulator model using the real process data and simulation results when compared with the simulated process characteristics that showed changes in the parameters. As a result the PI/NN controller error and was confirmed to reduce vibrations.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.41 no.7
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• pp.575-583
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• 2017

This paper describes the design and evaluation of a driving mode decision algorithm for automated driving for merge situations on highways. For the development of a highly automated driving control algorithm for merge situations, the driving mode decision is crucial for merging appropriately. There are two driving modes: lane keeping and lane changing (merging). The merge mode decision is determined based on the state of the surrounding vehicles and the remaining length of the merge lane. In the merge mode decision algorithm, merge possibility and the desired merge position are decided to change the lane safely and quickly. A safety driving envelope is defined based on the desired driving mode using the information on the surrounding vehicles' behaviors. To obtain the desired steering angle and longitudinal acceleration for maintaining the subject vehicle in the safe driving envelope, a motion planning controller is designed using model predictive control (MPC), with constraints that are decided considering the vehicle dynamics, safe driving envelope, and actuator limit. The proposed control algorithm has been evaluated via computer simulation studies.