Different noise sources change how adaptive systems handle disturbances

The Source of Disturbance Matters: External, Internal, and Control-Generated Noise in Adaptive Regulation

Artificial Intelligence

Summary

Adaptive systems try to keep things stable even when things get disturbed. The authors studied how different types of disturbances—outside disturbances, ongoing internal ones, and disturbances caused by the system’s own control actions—affect how well a system can stabilize itself. They found that ongoing internal disturbances cause the most trouble. Also, when the system’s corrections cause immediate extra costs, increasing those costs created complex effects on the system’s stability and activity. This work shows it’s important to consider where disturbances come from and when they happen to understand adaptive control better.

What this means in practice

  • For control system engineers: Improve adaptive controller designs by testing under different disturbance sources and orders to reduce regulatory load and exposure.
  • For robotics developers: Design robot controllers that consider internal and control-generated noise to maintain stable behavior under varying conditions.

Tested on simulated data.

Authors

Veronique Ziegler

Abstract

Adaptive regulation can itself perturb the state it is intended to stabilize. In replicated simulations of an adaptive agent, we compare external disturbance, persistent internally generated disturbance, and control-generated disturbance under regulation-first and disturbance-first ordering. Persistent internal disturbance produces the largest exposure and regulatory burden within the tested parameter grid. When positive controller updates generate an immediate disturbance cost, increasing that cost produces a nonmonotonic response: effective disturbance initially rises, variability across stochastic runs increases over an intermediate range, and corrective activity becomes strongly suppressed at higher costs. The results show how disturbance source and timing shape exposure and controller burden in this model. They motivate testing adaptive agents with distinct disturbance sources and assessing regulatory activity alongside exposure.
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