2026-07-02 オランダ・デルフト工科大学（TU Delft）
◆研究では、シミュレーションと飛行実験を組み合わせ、プロペラ損傷などの故障時に制御喪失へ至る過程を解析した結果、従来のような詳細な物理モデルを用いなくても、システムが限界に近づく兆候を高い精度で予測できることを実証した。この仕組みは、人間が痛みによって危険を察知し行動を変えることになぞらえ、「ドローンに痛覚を与える」技術と位置付けられている。将来的には、ドローンや航空機の予知保全、自動運転車、ロボット、インフラ監視など幅広い自律システムの安全性向上への応用が期待される。研究成果はProceedings of the National Academy of Sciences（PNAS）に掲載された。
＜関連情報＞
- https://www.tudelft.nl/en/2026/lr/giving-drones-a-sense-of-pain-a-new-way-to-predict-instability-before-it-happens
- https://www.pnas.org/doi/10.1073/pnas.2608847123
複雑なシステムにおける制御不能の早期警告信号 Early warning signals for loss of control in complex systems
Jasper J. van Beers, Marten Scheffer, Prashant Solanki, +2 , and Coen C. de Visser
Proceedings of the National Academy of Sciences Published:July 1, 2026
DOI:https://doi.org/10.1073/pnas.2608847123
Abstract
Maintaining stability in feedback systems, from aircraft and autonomous robots to biological and physiological systems, relies on monitoring their behavior and continuously adjusting their inputs. Incremental damage can make such control fragile. This tends to go unnoticed until a small perturbation induces instability (i.e., loss of control). Traditional methods in the field of engineering rely on accurate system models to compute a safe set of operating instructions, which become invalid when the, possibly damaged, system diverges from its model. Here we demonstrate that the approach of such a feedback system toward instability can nonetheless be monitored through dynamical indicators of resilience. This holistic system safety monitor does not rely on a system model and is based on the generic phenomenon of critical slowing down, shown to occur in the climate, biology, and other complex nonlinear systems approaching criticality. Our findings for engineered devices opens up a wide range of applications involving real-time early warning systems as well as an empirical guidance of resilient system design exploration, or “tinkering.” While we demonstrate the validity using drones, the generic nature of the underlying principles suggest that these indicators could apply across a wider class of controlled systems including reactors, aircraft, and self-driving cars.
