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This research proposes a novel method for detecting early signs of instability in autonomous systems by monitoring generic resilience indicators derived from critical slowing down. Validated through both simulations and real-world quadrotor experiments with damaged propellers, the approach offers an early warning mechanism against subtle degradation caused by wear or partial faults. For readers focused on AI safety and robust control theory, this work provides a systematic framework to ensure safe operation as autonomous systems evolve in unpredictable environments.
Indicators of resilience for autonomous control systems
arXiv:2609.18264v1 Announce Type: cross Abstract: As modern societies rely more on autonomous systems to facilitate daily life, assuring their safe operation is paramount. Naturally, there are many techniques available to predict and prevent system failures. However, the safety afforded by such schemes may become misaligned with the true system, which can change in unexpected ways - from partial faults to natural wear-and-tear - that subtly degrade its stability. The implications that such subtle changes have on autonomous system stability can be observed through generic indicators of resilience derived from critical slowing down, popular for anticipating catastrophic tipping points in natural systems. Here, we show how one can systematically design these generic indicators for nonlinear control systems and show how these can reflect loss of stability though simulations of canonical robotic systems wherein their proximity to instability is manipulated directly. These results are affirmed through real-world flight experiments of a quadrotor that is nudged towards instability by progressively damaging its propeller blades. Our results show that the implications of degraded resilience on closed-loop stability are evident well before they appear, for which the indicators of resilience derived here can provide an early warning.
Abstract:As modern societies rely more on autonomous systems to facilitate daily life, assuring their safe operation is paramount. Naturally, there are many techniques available to predict and prevent system failures. However, the safety afforded by such schemes may become misaligned with the true system, which can change in unexpected ways - from partial faults to natural wear-and-tear - that subtly degrade its stability. The implications that such subtle changes have on autonomous system stability can be observed through generic indicators of resilience derived from critical slowing down, popular for anticipating catastrophic tipping points in natural systems. Here, we show how one can systematically design these generic indicators for nonlinear control systems and show how these can reflect loss of stability though simulations of canonical robotic systems wherein their proximity to instability is manipulated directly. These results are affirmed through real-world flight experiments of a quadrotor that is nudged towards instability by progressively damaging its propeller blades. Our results show that the implications of degraded resilience on closed-loop stability are evident well before they appear, for which the indicators of resilience derived here can provide an early warning.
This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible
Systems and Control (eess.SY); Robotics (cs.RO)
arXiv:2609.18264 [eess.SY]
(or arXiv:2609.18264v1 [eess.SY] for this version)
https://doi.org/10.48550/arXiv.2609.18264 arXiv-issued DOI via DataCite (pending registration)
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