Semi-active control of aircraft landing gear system using H-infinity control approach
Bibliographic record
Abstract
The landing of an aircraft is one of the most critical operations because it directly affects the passenger safety and comfort. During landing, the aircraft fuselage undergoes excessive vibrations that cause the safety and the comfort problem and hence need to be suppressed quickly. A semi-active control system of a landing gear suspension by using Magnetorheological damper can solve the problem of excessive vibrations effectively. In this paper, a switching technique is developed in the simulation of the landing procedure which enables the system to switch from the single degree of freedom to three degrees of freedom system in order to simulate the sequential touching of the two wheels of the main landing gears and the nose landing gear wheels with the ground. A semi-active Magnetorheological damper is developed using two different controllers namely linear quadratic regulator and the H∞. Spencer model is used to predict the dynamic behavior of the Magnetorheological damper. The results of the designed controllers are compared to study the performance of the controllers in reducing the overshoot of the bounce response as well as the bounce rate response. The simulation results validated the improved performance of the robust controller compared to the optimal control strategy when the aircraft is subjected to the disturbances during 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".