Robust adaptive model-based compensator for the real-time hybrid simulation benchmark

Abstract

This study presents a robust adaptive model-based compensation framework for real-time hybrid simulation (RTHS), capable of minimizing synchronization errors with uncertain experimental substructures. The initial conditions of the compensator are defined using a nominal model of the transfer system without consideration of specimen–actuator interaction. Then, robust calibration of the compensator is obtained through offline numerical simulations using particle swarm optimization. The proposed methodology is validated in a virtual RTHS benchmark problem but incorporates more complex scenarios such as uncertain and nonlinear experimental substructures for the same compensator design. The results show excellent accuracy and robustness of the proposed methodology, with quick adaptation for different substructuring scenarios. Furthermore, this methodology proves that a robust compensator designed independently from the experimental substructure can be helpful to avoid tedious calibration and early tests of the physical specimen, with unintentional premature damage effects.