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

Abstract

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.

Performance study of model predictive control with reference prediction for real-time hybrid simulation

“…Several studies have been carried out using model predictive control (MPC) strategy (Yang et al, 2017;Pereida and Schoellig, 2018;Malalis et al, 2020). Tsokanas et al first confirmed the promising tracking performance and robustness of MPC in RTHS application (Tsokanas et al, 2020(Tsokanas et al, , 2021(Tsokanas et al, , 2022. Traditional and adaptive MPC combined with Kalman filter were proposed to predict the future plant states and handle the nonlinear system dynamics (Shao et al, 2022;Tsokanas et al, 2022).…”

A Comparison of Surrogate Modeling Techniques for Global Sensitivity Analysis in Hybrid Simulation

“…From the coupling of substructures, several challenges arise, e.g., time delays due to inherent transfer system dynamics or due to computational power needed to compute the NS response. Advanced control techniques [1][2][3][4] and model order reduction methods [5,6] have been used to tackle such issues. Albeit the challenges, the HS approach is beneficial since it can be used to experimentally study the inner workings of specific substructures over their linear regime and, hence, acquire realistic results but without constructing the entire considered system nor risking damaging it.…”

A comparison of surrogate modeling techniques for global sensitivity analysis in hybrid simulation

“…time delays due to inherent transfer system dynamics or due to computational power needed to compute the NS response. Advance control techniques [1,2] and model order reduction methods [3,4] have been used to tackle such issues. Albeit the challenges, the HS approach is beneficial since it can be used to experimentally study the inner workings of specific substructures over their linear regime and hence acquire realistic results but without constructing the entire considered system nor risking damaging it.…”