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Article Dans Une Revue Journal of Computer Languages Année : 2023

Easing the tuning of drone autopilots through a model-based framework

Résumé

Off-the-shelf open-source autopilots are customized by practitioners to satisfy their customer’s specific needs. When custom functions require low delays and/or fast frequency, in the order of magnitude of hundreds or a couple of thousand hertz, they may impact the behavior of the underlying stabilization loop. This paper proposes a tool chain able to extract a model using a Domain-Specific Language (DSL) based on AADL (Architecture Analysis & Design Language) semantics, extended with specific needs to capture the internal behavior of autopilots. This extraction is done directly during the compilation process of the autopilot. Then, we apply on the model of an autopilot a tool to assign offsets for offset-free systems.
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Dates et versions

hal-04357797 , version 1 (21-12-2023)

Identifiants

Citer

Soulimane Kamni, Antoine Bertout, Emmanuel Grolleau, Gautier Hattenberger, Yassine Ouhammou. Easing the tuning of drone autopilots through a model-based framework. Journal of Computer Languages, 2023, 77, pp.101240. ⟨10.1016/j.cola.2023.101240⟩. ⟨hal-04357797⟩
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