Physics-Based Reliability Modeling for Control Applications: Adaptative Control Allocation

Unmanned Aerial Vehicles (UAVs) are increasingly utilized across various industries, necessitating high reliability to ensure safety and reduce operational costs. This paper introduces a systematic methodology for physics-based reliability modeling tailored for UAV control applications, focusing on adaptive control allocation to optimize reliability. The study addresses the limitations of conventional degradation-independent behavior factor-based models, which often rely on inaccurate degradation models due to the lack of parameterization data sources and methods. By replacing time with stress-derived variables in the reliability function, this approach enables the combination of any time-dependent reliability functions with any stress-life relationships, allowing for real-time physics-based reliability assessments. The approach is demonstrated through the development and application of models for electronic speed controllers within a hexarotor UAV, using a virtual prototype for flight simulation. Simulation results reveal that physics-based models improve reliability prediction accuracy compared to conventional proportional hazard models, particularly due to their reliance on published and manufacturer's data for parameterization. The paper concludes by highlighting the need for future research to simplify the integration of stress factor online measurements, addressing the complexity and data requirements inherent in physics-based models.