Method for assessing the dependability of a distributed UAV control system based on an integral state model and forecasting
DOI:
https://doi.org/10.34121/1028-9763-2026-2-110-126Keywords:
unmanned aerial vehicles, dependability, integral state model, forecasting, distributed control system, risk, time delays, directed graphAbstract
Distributed control systems for unmanned aerial vehicles (UAVs), which integrate onboard computing modules, sensors, communication channels, and ground/edge services, are characterized by increased complexity in assessing their functional state under conditions of disturbances, component degradation, and potential cyber impacts. The paper proposes a method for assessing the dependability of such systems based on an integral state model and forecasting the dynamics of a generalized indicator. The system structure is formalized as a directed graph of interacting nodes, each associated with a local state vector that includes indicators of reliability, availability, functional correctness of control, cyber resilience, and time delays. To enable a transition from local characteristics to a system-level assessment, a normalized integral dependability metric in the range [0;1] has been formed, taking into account the structural importance of nodes. The predictive component provides an estimation of the future values of the integral indicator over a finite time horizon, identifies degradation trends, and quantitatively estimates the potential extent of critical-threshold violations. This enables a transition from reactive to proactive decision-making. Numerical simulation for scenarios of nominal operation, gradual degradation, sudden partial failure, and combined impact confirmed that forecasting provides a time reserve for adjusting control modes and redistributing resources, thus reducing the probability of losing an acceptable level of dependability. According to the experimental results, the average prediction lead time gain is 7.3 discretization steps (approximately 22 % reduction in reaction time) compared with an approach without forecasting. Таbl.: 2. Figs.: 5. Refs.: 23 titles.
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