Dynamic Force Loading Strategy for Effective Force Testing Considering Natural Velocity Feedback Compensation and Nonlinearity

Author(s):  
Zhen Wang ◽  
Yong Ding ◽  
Aming Shi ◽  
Xizhan Ning ◽  
Bin Wu

The effective force testing is a promising seismic testing method for evaluating the structural dynamic response to earthquakes for conciseness and efficiency. However, two challenging loading issues are associated with this method, i.e. the natural velocity feedback (NVF) and nonlinearities related to the interaction between the loading system and specimen, thereby hindering its development and extensive applications. To address these issues, this study proposes a dynamic force loading strategy using a hybrid algorithm with linear compensation for NVF and model reference adaptive control via the minimal control synthesis (MCS) method. Online identification of linear compensation gain in preliminary tests is conceived based on the gradient descent method. A series of numerical simulations on a nonlinear loading system model with linear/trilinear single/two degree(s)-of-freedom specimens are conducted using five loading strategies, including linear and nonlinear compensations and MCS method. Comparative studies show that the proposed method and nonlinear compensation strategy outperform the other three methods, and sometimes the proposed method performs best. In summary, the proposed method is promising because of its accuracy and robustness as well as its ease of implementation and cost-effectiveness.

1994 ◽  
Author(s):  
S. LeQuoc ◽  
Y. F. Xiong ◽  
R. M. H. Cheng ◽  
H. Q. Zhang
Keyword(s):  

Author(s):  
Hao Jiang ◽  
Haijun Wu ◽  
Liang Yu ◽  
Weikang Jiang

The errors of the force identification vary with different spatial locations of the response measurements on machinery. This investigation proposes a method to identify the structural dynamic force with the use of the distributed response. An optimized sensor placement is obtained by using a set of local orthogonal polynomials to approximate the response distribution of the chosen region and choosing the Gaussian quadrature points as the sensor locations. Then the forward model based on the reconstruction of distributed response is established and the dynamic force can be obtained by an appropriate regularization method. Numerical simulations of models with planar and curved surfaces are presented to validate the method. It is found that the proposed method can effectively reduce the influence of noise and improve the precision of the force identification. The method is also validated by an experiment and an accurate recognition of the forces is observed. This paper provides a new perspective on the force identification procedure based on the distributed response.


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