Co-Design for Real-Time Adaptability: Methodology, Implementation, and Real-Time Performance on a Morphing Wind Turbine

Abstract Large-scale wind energy-harvesting systems operate in highly variable environments and face long design and manufacturing cycles that often require design freezes before high-certainty modeling is possible. Both challenges can be addressed by incorporating real-time plant and controller adaptability, recognizing that adaptability comes at a cost. The proposed co-design framework aims to maximize the lifetime expected profit of a wind energy system (though its mathematical underpinnings are generalizable to other energy-harvesting devices), accounting for a low-complexity surrogate model of system performance, a statistical model of the environment, a characterization of how modeling uncertainty diminishes over the design cycle, and cost models that consider the price of adaptability and engineering labor. By jointly considering plant and controller adaptability, this framework trades off high-cost/high-reward plant adaptability with lower-cost/lower-reward controller adaptability. This co-design framework is coupled with an online control strategy that performs real-time adaptation under constraints. To evaluate this approach, we focus on the segmented ultralight morphing rotor (SUMR) wind turbine. Applying the co-design framework to the SUMR using the aforementioned surrogate model, the expected lifetime profit increases by 16.7% when adaptability is optimized. To validate the surrogate model, a 24-h dynamic simulation was conducted using the optimized design. The SUMR system with an online adaptive controller generated 4.4% more energy than the system with a nonadaptive controller, demonstrating the impact of adaptability on performance. Dynamic simulation predictions closely match those of the surrogate model used in the co-design optimization, further validating the approach.