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An adaptive delay compensation method based on a discrete system model for real-time hybrid simulation

  • Wang, Zhen (School of Civil Engineering and Architecture, Wuhan University of Technology) ;
  • Xu, Guoshan (Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education, Harbin Institute of Technology) ;
  • Li, Qiang (Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education, Harbin Institute of Technology) ;
  • Wu, Bin (School of Civil Engineering and Architecture, Wuhan University of Technology)
  • Received : 2019.07.19
  • Accepted : 2019.12.17
  • Published : 2020.05.25

Abstract

The identification of delays and delay compensation are critical problems in real-time hybrid simulations (RTHS). Conventional delay compensation methods are mostly based on the assumption of a constant delay. However, the system delay may vary during tests owing to the nonlinearity of the loading system and/or the behavioral variations of the specimen. To address this issue, this study presents an adaptive delay compensation method based on a discrete model of the loading system. In particular, the parameters of this discrete model are identified and updated online with the least-squares method to represent a servo hydraulic loading system. Furthermore, based on this model, the system delays are compensated for by generating system commands using the desired displacements, achieved displacements, and previous displacement commands. This method is more general than the existing compensation methods because it can predict commands based on multiple displacement categories. Moreover, this method is straightforward and suitable for implementation on digital signal processing boards because it relies solely on the displacements rather than on velocity and/or acceleration data. The virtual and real RTHS results show that the studied method exhibits satisfactory estimation smoothness and compensation accuracy. Furthermore, considering the measurement noise, the low-order parameter models of this method are more favorable than that the high-order parameter models.

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