Simulation and 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 non-linear 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 state-space matrices. The obtained parameters are then optimized with the Levenberg—Marquardt 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.
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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 | 0.001 |
| 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".