Supporting Stability Analysis of Aircraft Flight Control Laws

Traditionally, flight control laws are verified, among other analyses, by demonstrating robust stability in the flight envelope (namely, in the region of operation of aircraft). Stability requirements are given in terms of gain and phase margins. These margins give an indication of the robustness of the control laws in presence of unmodeled effects and uncertainty. This paper proposes an AI-based approach to support the stability analysis tasks by identifying a structured partition of the flight envelope, where each region of the partition exhibits locally homogeneous stability characteristics. Building on recursive partitioning methods from Machine Learning, the proposed approach leverages the interpretability of tree-based models to facilitate human expert validation and usage of the obtained results.