A Framework for Fused Experimental/Numerical Plant and Control System Optimization Using Iterative G-Optimal Design of Experiments

This paper presents a methodology for optimally fusing experiments and numerical simulations in the design of a combined plant and control system. The proposed methodology uses G-optimal Design of Experiments to balance the need for experimental data with the expense of collecting a multitude of experimental results. Specifically, G-optimal design is used to first select a batch of candidate experimental configurations, then determine which of those points to test experimentally and which to numerically simulate. The optimization process is carried out iteratively, where the set of candidate design configurations is shrunken at each iteration using a Z-test, and the numerical model is corrected according to the most recent experimental results. The methodology is presented on a model of an airborne wind energy system, wherein both the center of mass location (plant parameter) and trim pitch angle (controller parameter) are critical to system performance.