An integrated interpretable control effectiveness learning and nonlinear control allocation methodology for overactuated aircrafts
arXiv:2606.13794v1 Announce Type: cross Abstract: Nonlinear dynamics and the strong couplings that arise between multiple effectors undermine the assumptions behind conventional, linear control allocation techniques. When flight enters regimes where nonlinear effects dominate, linear allocators exhibit reduced accuracy due to increased model mismatch, which subsequently degrades performance and robustness of the flight control system. High fidelity onboard models and black box data driven approaches can recover accuracy across the flight envelope, but respectively impose computational burdens
This research is emerging as AI and machine learning techniques become more robust and computationally efficient, enabling their application to real-time, safety-critical systems like flight control.
Improving control systems for overactuated aircraft with AI directly enhances performance, safety, and efficiency, which is crucial for the expanding use of advanced aerial platforms, including drones and future autonomous air mobility.
The integration of interpretable machine learning into flight control shifts from purely model-based designs towards adaptive, data-driven systems that can better handle complex aerodynamic nonlinearities.
- · Aerospace and defence companies
- · AI/ML developers in control systems
- · Autonomous aircraft manufacturers
- · Military aerospace
- · Developers of legacy linear control systems
- · Manufacturers unable to adopt new AI control paradigms
Aircraft, especially advanced drones and nascent eVTOLs, will achieve greater agility, reliability, and fuel efficiency.
This improved performance could accelerate the development and deployment of various autonomous air systems for logistics, surveillance, and combat.
Enhanced autonomous flight capabilities could further integrate into national defence strategies, boosting the effectiveness of uncrewed aerial vehicles and reducing human pilot risk in various missions.
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Read at arXiv cs.AI