Adaptive model predictive control for actuation dynamics compensation in real-time hybrid simulation

Real-time hybrid simulation is an experimental method used to obtain the dynamic response of a system whose components consist of loading-rate-sensitive physical and numerical substructures. The coupling of these substructures is achieved by actuation systems, i.e., an arrangement of motors or actuators, which are responsible for continuously synchronizing the interfaces of the substructures and are commanded in closed-loop control setting. To ensure high fidelity of such hybrid simulations, performing them in real-time is necessary. However, real-time hybrid simulation poses challenges as the inherent dynamics of the actuation system introduce time delays, thus modifying the dynamic response of the investigated system and hence compromising the simulation's fidelity and trust in the obtained response quantities. Therefore, a reference tracking controller is required to adequately compensate for such time delays.In this study, a novel tracking controller is proposed for dynamics compensation in real-time hybrid simulations. It is based on an adaptive model predictive control approach, a linear time-varying Kalman filter, and a real-time model identification algorithm. Within the latter, auto-regressive exogenous polynomial models are identified in real-time to estimate the changing plant dynamics and used to update the prediction model of the tracking controller. A parametric virtual real-time hybrid simulation case study is used to validate the performance and robustness of the proposed control scheme. Results demonstrate the effectiveness of the proposed controller for real-time hybrid simulations.