Geometric Disturbance Observer Based Nonlinear Model Predictive Control of a Quadrotor
Bibliographic record
Abstract
Abstract This letter presents a geometric disturbance observer-based nonlinear model predictive control (NMPC) architecture for quadrotor trajectory tracking. The proposed approach couples a geometric extended-state extended Kalman filter (ES-EKF) formulated on the SO(3) manifold with a disturbance-aware predictive controller. By embedding explicit force and torque disturbance states into the continuous-time model, the ES-EKF obtains real-time estimates of six degrees-of-freedom (DOF) perturbations. These disturbance estimates are injected as known parameters into the NMPC’s prediction model at each sampling instant, enabling proactive compensation within the receding-horizon optimization. Simulations are conducted on different trajectories with varied flight conditions subjected to periodic 6DOF perturbations. The proposed ES-EKF-NMPC framework reduces position root mean square error by 60% on average compared to baseline NMPC without disturbance feedback. These results demonstrate that the proposed architecture offers disturbance-resilient control for under-actuated unmanned aerial vehicles while handling constraints.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".