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Smart Structures and Systems
  Volume 14, Number 6, December 2014, pages 1105-1129
DOI: http://dx.doi.org/10.12989/sss.2014.14.6.1105
 


Model updating with constrained unscented Kalman filter for hybrid testing
Bin Wu and Tao Wang

 
Abstract
    The unscented Kalman filter (UKF) has been developed for nonlinear model parametric identification, and it assumes that the model parameters are symmetrically distributed about their mean values without any constrains. However, the parameters in many applications are confined within certain ranges to make sense physically. In this paper, a constrained unscented Kalman filter (CUKF) algorithm is proposed to improve accuracy of numerical substructure modeling in hybrid testing. During hybrid testing, the numerical models of numerical substructures which are assumed identical to the physical substructures are updated online with the CUKF approach based on the measurement data from physical substructures. The CUKF method adopts sigma points (i.e., sample points) projecting strategy, with which the positions and weights of sigma points violating constraints are modified. The effectiveness of the proposed hybrid testing method is verified by pure numerical simulation and real-time as well as slower hybrid tests with nonlinear specimens. The results show that the new method has better accuracy compared to conventional hybrid testing with fixed numerical model and hybrid testing based on model updating with UKF.
 
Key Words
    model updating; real-time hybrid testing; unscented Kalman filter; bound constraint
 
Address
Bin Wu: Key Lab of Structures Dynamic Behavior and Control (Harbin Institute of Technology), Ministry of Education, Harbin, 150090, China;
Harbin Institute of Technology, Harbin, China
Tao Wang: Key Lab of Structures Dynamic Behavior and Control (Harbin Institute of Technology), Ministry of Education, Harbin, 150090, China;
Harbin Institute of Technology, Harbin, China;
Heilongjiang University of Science and Technology, Harbin, China
 

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