Cascaded Extended‐State‐Observer‐Based Synergetic Control for Quadcopter Translational Dynamics
Bibliographic record
Abstract
ABSTRACT This paper presents an approach to quadcopter position control, utilizing a Cascaded Extended State Observer (CESO) integrated with synergetic control. The proposed control strategy enhances the quadcopter's stability and tracking accuracy by estimating and compensating for aerodynamic disturbances and drag forces to a significant extent, which are challenging to measure or model analytically. This extent increases as the levels of the cascaded structure grow, progressively enhancing both accuracy and compensation capability. An efficient tuning approach is introduced in the paper for tuning multiple ESOs in a cascaded structure that uses hierarchical gain reduction, ensuring distinct frequency ranges for each observer. This achieves a rapid initial estimation while reducing noise in later stages, enhancing stability and robustness. The CESO framework, combined with synergetic control, offers a robust solution, minimizing mean squared error and control effort while improving disturbance rejection. The PX4‐ROS2 architecture was used to test our system in Gazebo and on a custom‐built quadcopter experimentally, validating the efficacy of the proposed control scheme. This study contributes significantly to the development of advanced control techniques for unmanned aerial vehicles, emphasizing practical implementation and adaptability in real‐world scenarios.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".