Prediction of Main Rotor, Tail Rotor and Engine Parameters from Flight Tests
Bibliographic record
Abstract
In the framework of this research project, the main rotor torque, tail rotor torque, engine torque and main rotor speed of a helicopter in forward flight are estimated by using a state space model from flight tests data. The state space model inputs are nonlinear terms made of combinations of pilot controls and helicopter states. The model simulates the helicopter outputs while knowing the states and controls at all times. It was also implemented as a prediction tool, for possible use in an envelope protection flight control system in which the states, controls and outputs are known at the present time, and predict the future helicopter states and controls following to pilot controls time history. The state space model parameters are identified by using the subspace identification method, a relatively recent non-iterative algorithm which constructs an observability matrix from input and output data and uses this matrix to obtain the statespace matrices. The obtained parameters are then optimized with the LevenbergMarquardt output-error method. A comparison of the results with and without optimization is also conducted. The results show that the subspace method provides a good estimate of the outputs within the FAA tolerance bands and that these results can further be improved by use of the minimization algorithm. The generated model using the subspace method is found to be very good for prediction applications, which makes it a promising model for flight control simulator applications.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".