Fault-Tolerant Control of a Boeing 747-100/200 Based on a Laguerre Function-Based MPC Scheme
Bibliographic record
Abstract
Fault-tolerant control (FTC) is of paramount for the safety of aircraft in the presence of any faults due to its safety-critical property. An active FTC scheme is presented and implemented for the nonlinear benchmark model of Boeing 747-100/200 to safely land aircraft even in the presence of major actuator faults/failures. Furthermore, for the improvement of its on-line fault-tolerant capability, Laguerre functions based MPC (LF-MPC) scheme is further developed and applied to the fault-tolerant controller design. The benefits of LF-MPC scheme are 1) redistributing the control efforts efficiently to the remaining functional actuators in case of faults, 2) increasing the on-line fault-tolerant capability by reducing optimized parameters. In addition, time delay of fault detection and diagnosis (FDD) information is also considered and integrated into the feedback control loop. The effectiveness is demonstrated on the well-known Boeing 747-100/200 benchmark through a landing phase with elevators stuck in the process of landing.
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 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) | 0.001 | 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".