Early warning signals for loss of control

2025-12-23

AI Summary

Feedback systems—such as unmanned aerial vehicles (UAVs) and autonomous vehicles—are vulnerable to instability induced by progressive structural degradation; conventional model-based safety control methods fail under model mismatch. Method: We propose a model-agnostic, real-time loss-of-control early-warning framework that systematically introduces critical slowing down—a universal dynamical resilience indicator—into engineering safety monitoring. Using sliding-window time-series analysis (e.g., autocorrelation and variance amplification), it quantifies stability degradation online without requiring prior system knowledge. Contribution/Results: Validated in closed-loop experiments on a physical UAV platform, the method detects incipient instability caused by structural damage seconds to tens of seconds before failure. Generalization tests confirm its applicability across diverse controlled systems—including aircraft and nuclear reactors—thereby transcending the traditional paradigm reliant on high-fidelity modeling for safety verification.

Technology Category

• Natural Language Processing: Safety and Robustness
• Intelligent Robots: State Estimation
• Multiagent Systems: Multiagent Systems under Uncertainty

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Large-scale security measurements

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 towards 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.

Problem

• Monitors system instability without needing accurate models
• Uses critical slowing down as early warning for control loss
• Applies to diverse systems like drones, reactors, and autonomous vehicles

Innovation

• Model-free resilience monitoring via critical slowing down
• Real-time early warning system for control loss
• Holistic safety indicators applicable across diverse systems