This paper presents a novel data-driven nested optimization framework that aims to solve the problem of coupling between plant and controller optimization. This optimization strategy is tailored towards instances where a closed-form expression for the system dynamics is unobtainable and simulations or experiments are necessary. Specifically, Bayesian Optimization, which is a data-driven technique for finding the optimum of an unknown and expensive-to-evaluate objective function, is employed to solve the nested optimization problem. The underlying objective function is modeled by a Gaussian Process (GP); then, Bayesian Optimization utilizes the predictive uncertainty information from the GP to decide the best subsequent control or plant parameters. The proposed framework differs from the majority of co-design literature where there exists a closed-form model of the system dynamics. We validate the proposed framework for Altaeros’ Buoyant Airborne Turbine (BAT). We choose the horizontal stabilizer area and longitudinal center of mass relative to center of buoyancy (plant parameters) and the pitch angle set-point (controller parameter) as our decision variables. Our results demonstrate that plant and control parameters converge to optimal values within only a few iterations.

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