Early warning signals for loss of control

TL;DR

This paper introduces a model-free early warning system for loss of control in feedback systems, based on dynamical indicators of resilience like critical slowing down, applicable across various engineered and biological systems.

Contribution

It demonstrates that monitoring the approach to instability can be achieved without system models, using generic indicators of critical slowing down, validated on drones and applicable to diverse systems.

Findings

Early warning signals successfully detected loss of control in drones.

Model-free indicators provide real-time resilience monitoring.

Applicable to various complex nonlinear systems.

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.