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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".