Design of an Actuation Controller for Physical Substructures in Stochastic Real-Time Hybrid Simulations

摘要

Real-time hybrid simulation is a method to obtain the response of a system subjected to dynamic excitation by combining loading-rate-sensitive numerical and physical substructures. The interfaces between physical and numerical substructures are usually implemented using closed-loop-controlled actuation systems. In current practice, the parameters that characterize the hybrid model are deterministic. However, the effect of uncertainties may be significant. Stochastic hybrid simulation is an extension of the deterministic hybrid simulation where the parameters of the system are treated as random variables with known probability distributions. The results are probability distributions of the structural response quantities of interest. The arising question is to what extent does the actuation control system at the interface between physical and numerical substructures affect the outcomes of stochastic hybrid simulations. This question is most acute for real-time hybrid simulations. The response of a benchmark stochastic prototype to random excitation will be computed. Then, a part of the prototype will be replaced by a hybrid model whose substructure interfaces are actuated in closed-loop control. A controller that guarantees robustness and stability of the interfaces will be designed. The parameters of this hybrid model will be treated as random variables in repeated real-time hybrid response simulations to the same random excitation. The difference between the prototype and hybrid model responses will be used to determine if the controller design has an effect on the simulation outcomes, to predict such effects, and to propose guidelines for real-time controller design such that it has a predictable effect on the hybrid simulation. Additional criteria based on peak and root mean square tracking errors, as well as energy errors, are addressed in order to assess the overall system performance. Based on simulation data, surrogate models will be developed. Multiple additional runs of the surrogate models will give insight into the robustness and performance of the control system under uncertainties. Global sensitivity analysis of the overall system response will also be performed, identifying the most sensitive stochastic input variables. Cross-check validation of the results will take place using different meta-modeling techniques.