Robust decentralized adaptive compensation for the multi-axial real-time hybrid simulation benchmark

Abstract

Real-time hybrid simulation (RTHS) is a powerful and highly reliable technique integrating experimental testing with numerical modeling for studying rate-dependent components under realistic conditions. One of its key advantages is its cost-effectiveness compared to large-scale shake table testing, which is attained by selectively conducting experimental testing on critical parts of the analyzed structure, thus avoiding the assembly of the entire system. One of the fundamental advancements in RTHS methods is the development of multi-dimensional dynamic testing. In particular, multi-axial RTHS (maRTHS) aims to prescribe multi-degree-of-freedom (MDOF) loading from the numerical substructure over the test specimen. Under these conditions, synchronization is a significant challenge in multiple actuator loading assemblies. This study proposes a robust and decentralized adaptive compensation (RoDeAC) method for the next-generation maRTHS benchmark problem. An initial calibration of the dynamic compensator is carried out through offline numerical simulations. Subsequently, the compensator parameters are updated in real-time during the test using a recursive least squares adaptive algorithm. The results demonstrate outstanding performance in experiment synchronization, even in uncertain conditions, due to the variability of reference structures, seismic loading, and multi-actuator properties. Notably, this achievement is accomplished without needing detailed information about the test specimen, streamlining the procedure and reducing the risk of specimen deterioration. Additionally, the tracking performance of the tests closely aligns with the reference structure, further affirming the excellence of the outcomes.