Automatic autopilot tuning framework using genetic algorithms and system identification
This paper presents a comprehensive framework for offline optimization of tuning parameters in unmanned aerial vehicle (UAV) flight controllers. The framework uses system identification to create a simplified flight dynamics model, followed by control law matching to ensure the simulated controller&...
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| Veröffentlicht in: | Aerospace science and technology Jg. 157; S. 109779 |
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| Hauptverfasser: | , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Elsevier Masson SAS
01.02.2025
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| ISSN: | 1270-9638 |
| Online-Zugang: | Volltext |
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| Zusammenfassung: | This paper presents a comprehensive framework for offline optimization of tuning parameters in unmanned aerial vehicle (UAV) flight controllers. The framework uses system identification to create a simplified flight dynamics model, followed by control law matching to ensure the simulated controller's output closely replicates real-world autopilot commands. The optimization phase employs genetic algorithms to tune parameters based on a defined cost function that incorporates performance requirements. Each stage, from flight dynamics model development to optimization, is validated to ensure enhanced controller performance. Finally, real-world flight tests confirm the effectiveness of the optimized controller, demonstrating the validity of the proposed framework for autopilot tuning optimization.
•Genetic Algorithms optimize UAV autopilot tuning for enhanced flight performance.•System identification simplifies UAV flight dynamics model creation for tuning.•Real-world flight tests validate the proposed autopilot tuning framework.•The framework adapts to various UAV controllers and vehicle configurations.•Optimized controller parameters improve stability and performance in UAVs. |
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| ISSN: | 1270-9638 |
| DOI: | 10.1016/j.ast.2024.109779 |