Co-Design for Real-Time Adaptability: Methodology and Wind Energy Case Study⁎⁎This research was supported by the National Science Foundation Award Number 2321698

• Published in: IFAC-PapersOnLine Vol. 58; no. 28; pp. 816 - 821
• Main Authors: Fine, Jacob B., Holbrook, Ian, Vermillion, Chris
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 2024
• Subjects: adaptive control; control of power systems; Control of renewable energy resources; Energy systems; Hardware/software co-design; Optimal operation; control of power systems; Energy systems; Control of renewable energy resources; Optimal operation; Hardware/software co-design; adaptive control
• ISSN: 2405-8963; 2405-8963
• DOI: 10.1016/j.ifacol.2025.01.076

This work presents a unique control co-design formulation that explicitly optimizes the level of real-time adaptability in both the physical design and control parameters. Adaptability is particularly important for large-scale energy-harvesting systems, which operate in highly variable environments and are subject to long design and manufacturing cycles. Here, physical components and software often must be “frozen” relatively early, sometimes well-before the system’s dynamics have been fully characterized. The proposed co-design framework performs a maximization of expected profit, accounting for a low-complexity surrogate model of the system’s performance, a statistical model of the environment, a statistical characterization of how modeling uncertainty diminishes over the design cycle, and cost models that consider the price of adaptability. This co-design framework is coupled with an online control strategy that performs the real-time adaptation, subject to constraints. To evaluate this approach, we focus on the segmented ultralight morphing rotor (SUMR) described in Noyes et al. (2020), Zalkind et al. (2017), Kianbakht et al. (2022), and Ananda et al. (2018). Applying the co-design framework to the SUMR, and utilizing the aforementioned performance surrogate model, the expected lifetime profit is shown to increase by 6.5% when the level of adaptability is optimized. Overa24-hour dynamic simulation, the predictions based on the simplified surrogate model are shown to deviate by less than 1% relative to the results of the higher-fidelity dynamic model (which is suitable for 24-hour simulations but not for long-term profitability projections).