• Smart Structures and Systems
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• v.20 no.5
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• pp.619-636
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• 2017

Magnetorheological (MR) damper is a type of controllable device widely used in vibration mitigation. This device is highly nonlinear, and exhibits strongly hysteretic behavior that is dependent on both the motion imposed on the device and the strength of the surrounding electromagnetic field. An accurate model for understanding and predicting the nonlinear damping force of the MR damper is crucial for its control applications. The MR damper models are often identified off-line by conducting regression analysis using data collected under constant voltage. In this study, a MR damper model is integrated with a model for the power supply unit (PSU) to consider the dynamic behavior of the PSU, and then a real-time nonlinear model updating technique is proposed to accurately identify this integrated MR damper model with the efficiency that cannot be offered by off-line methods. The unscented Kalman filter is implemented as the updating algorithm on a cyber-physical model updating platform. Using this platform, the experimental study is conducted to identify MR damper models in real-time, under in-service conditions with time-varying current levels. For comparison purposes, both off-line and real-time updating methods are applied in the experimental study. The results demonstrate that all the updated models can provide good identification accuracy, but the error comparison shows the real-time updated models yield smaller relative errors than the off-line updated model. In addition, the real-time state estimates obtained during the model updating can be used as feedback for potential nonlinear control design for MR dampers.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.32 no.1
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• pp.45-54
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• 2019

The damage detection method using the extended Kalman filter(EKF) technique has been continuously used since EKF can estimation the responses of the damaged building structure and the stiffness of the structure. However, in the use of EKF, the requirement of setting the initial paramters P, Q, and R has caused the divergence and instability of the state vector, and various researches have been conducted to determine theses parameters. In this paper, adaptive extended Kalman filter(AEKF) method is proposed to solve the problem of setting the values of P, Q, and R, which are important parameters determining the convergence performance of the EKF state vector. By using the AEKF method proposed in this study, the P, Q, and R parameters are updated every k steps. The proposed algorithm is applied for the estimation of stiffness and the damage detection of 3-DOF problem. Based of the verification, it can be found that the selection process for the values of P, Q, and R can improve the convergence performance of EKF.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.43 no.10
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• pp.902-909
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• 2015

In this paper, monovision based relative navigation for a satellite proximity operation is studied. The chaser satellite only uses one camera sensor to observe the target satellite and conducts image tracking to obtain the target pose information. However, by using only mono-vision, it is hard to get the depth information which is related to the relative distance to the target. In order to resolve the well-known difficulty in computing the depth information with the use of a single camera, the active contour method is adopted for the image tracking process. The active contour method provides the size of target image, which can be utilized to indirectly calculate the relative distance between the chaser and the target. 3D virtual reality is used in order to model the space environment where two satellites make relative motion and produce the virtual camera images. The unscented Kalman filter is used for the chaser satellite to estimate the relative position of the target in the process of glideslope approaching. Closed-loop simulations are conducted to analyze the performance of the relative navigation with the active contour method.