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Smart Structures and Systems
  Volume 20, Number 5, November 2017 , pages 619-636
DOI: https://doi.org/10.12989/sss.2017.20.5.619
 


Real-time model updating for magnetorheological damper identification: an experimental study
Wei Song, Saeid Hayati and Shanglian Zhou

 
Abstract
     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.
 
Key Words
    nonlinear model identification; real-time; model updating; magnetorheological (MR) damper; power supply; unscented Kalman filter (UKF)
 
Address
Wei Song, Saeid Hayati and Shanglian Zhou: Department of Civil, Construction and Environmental Engineering,
The University of Alabama, Tuscaloosa, AL, USA 35487
 

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