Internal-Time Gating for Uncertainty-Aware Switching Control
Abstract
This work presents an internal-time–based gating mechanism for uncertainty-aware switching control systems. The proposed framework addresses stability and safety challenges in learning-enabled and adaptive controllers by regulating the frequency of high-impact control actions, rather than enforcing per-step admissibility constraints. An internal-time variable is introduced whose evolution is modulated by a predictive uncertainty measure. Access to high-impact (potentially non-contractive) control modes is permitted only when the accumulated internal time exceeds a prescribed threshold and uncertainty is sufficiently low. This mechanism induces an endogenous switching signal between safe and high-impact dynamics. The closed-loop system is modeled as a two-mode switched system, and stability is analyzed using Lyapunov methods and average dwell-time theory. Explicit conditions are derived that guarantee uniform ultimate boundedness and asymptotic stability in the disturbance-free case. Constructive design and calibration rules are provided to estimate contraction and expansion bounds from short-horizon data and translate them into explicit parameters for the internal-time threshold. The framework is fully self-contained from a control-theoretic perspective and does not rely on any physical interpretation of time or entropy beyond their operational use as uncertainty proxies. It complements existing Lyapunov- and barrier-based safety approaches by shifting part of the safety burden from instantaneous action filtering to temporal regulation of mode switching. The proposed method is applicable to a broad class of learning-based, adaptive, and hybrid control systems. Relation to Prior Work This work builds conceptually on the author’s foundational framework on internal-time and endogenous governance mechanisms, available as a separate Zenodo record. The present contribution is technically independent and focuses exclusively on control-theoretic formulation, stability analysis, and constructive design rules. Notes This record provides a standalone technical contribution with an independent DOI. The associated foundational work is cited for conceptual motivation only and is not required to understand or apply the results presented here.
Fields of Study & Subjects
Control and Stability of Dynamical Systems
Stability and Control of Uncertain Systems
Model Reduction and Neural Networks
Publication details
- Journal / source
- Zenodo (CERN European Organization for Nuclear Research)
- Publication date
- 9 January 2026
- Language
- EN