Model Predictive Control of Fixed Wing Aircraft Using a Disturbance Observer Approach
Bibliographic record
Abstract
Abstract This paper develops a novel cascading Proportional-Integral-Derivative (PID) with Model Predictive Control (MPC) formulation for lateral control of a fixed wing aircraft in the presence of a constant load disturbance, with the consideration of actuator constraints. A Constrained Quadratic Programming (QP) problem is used to solve this MPC problem, via the Primal-Dual procedure. Furthermore, a disturbance observer is utilized to estimate this disturbance so that the setpoint calculation can be adjusted accordingly. Numerical simulations demonstrate steady-state tracking of the aircraft’s roll angle whilst rejecting this disturbance. In addition, heading (yaw) control is implemented via the outer PID loop, and perfect tracking is achieved for this as well. Throughout the entire simulation, the aircraft’s control inputs (aileron and rudder) do not violate their position and rate constraints, thus demonstrating the successful performance of the QP algorithm.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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) | 0.000 | 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".