Online optimization of adjustable settings for agricultural machinery assisted by digital twin

To address the complex and variable agricultural production, the adjustable settings on agricultural machinery have become increasingly numerous. However, determining the most applicable set of settings from thousands of possible combinations has emerged as a new challenge, one that is difficult to achieve through traditional experience-based decision-making and feedback control. This study analyzed the characteristics of the online optimization problem for adjustable settings in agricultural machinery, framing it as a single-objective cost optimization problem with a continuous feasible region and multi-modality. By introducing Digital Twin (DT) technology, a DT-assisted online optimization method (DTAOO) is proposed to search for the optimal set of setting. Specifically, DTAOO consists of two parts. One part involves the building of the DT, using an ensemble modeling combined with data augmentation to quickly establish and reconstruct the DT based on small sample data collected from physical space. The other part is the DT-assisted evolutionary algorithm (DTAEA), which employs the DT to predictively evaluate candidate solutions in a virtual space. This assists the evolutionary algorithm in searching for the most promising candidate solutions. In numerical experiments, the performance of DTAOO was evaluated through a series of benchmark problems and compared with other representative peer algorithms. Experimental results show that DTAOO achieved better results than peer algorithms on some complex benchmark problems. On multi-peak benchmark tests with uncertainty, DTAOO demonstrated a significant advantage. By applying DTAOO to optimize the settings related to the threshing process of a corn combine harvester, the grain breakage rate was reduced and working efficiency was improved, demonstrating the practical applicability of DTAOO. This study contributes to searching the optimal set of adjustable settings for agricultural machinery in complex production environments, offering the potential to improve production quality and efficiency without additional costs, and providing a reference for the operation, optimization and control of intelligent agricultural production systems.