Flight Dynamics and Control of UAS-S4 and S45
Bibliographic record
Abstract
In this paper, new methodologies are presented for flight dynamics and control model of two unmanned aerial systems (UAS) designed and developed in Mexico by Hydra Technologies. These two UAS are the UAS-S4 and UAS-S45. In fact, the aerodynamic model was developed by calculating the aerodynamic coefficients (lift, drag and pitching moment) using four various methods and their corresponding software. Two of these numerical methodologies are based on experimental data, therefore are semi-empirical, and programmed in two codes: the well-known DATCOM (developed by the US Air Force Flight Laboratories) and FDerivatives, that was developed by our team at the Laboratory of Active Controls, Avionics and AeroServoElasticity LARCASE. In both semi-empirical methodologies, the main geometrical characteristics of the wing, wing-body and all aircraft components are given as inputs to the two software, which gives as outputs the aerodynamic coefficients with their corresponding stability and control derivatives for various flight conditions. A third methodology is programmed using a low fidelity aerodynamics code called Tornado, which uses the Vortex Lattice Method (VLM). A fourth methodology is programmed using a high-fidelity aerodynamics code called Fluent in Ansys. This methodology used the Navier-Stokes equations. Therefore, a comparison is presented between the aerodynamic coefficients for a range of various flight cases, obtained using the three low-fidelity codes (DATCOM, FDerivatives and Tornado) and the ANSYS-FLUENT code. As the results were found to be close, it was considered that the estimation of the aerodynamic model was accurate. Then, this aerodynamic model was combined with the propulsion, structures and actuators models with the aim to develop a global flight dynamics model for each of the UAS. Then, a new controller methodology was performed with four combined theories: the Linear Quadratic Regulator (LQR), the Proportional Integral with reference feedforward (PI-FF), the Generalized Extended State Observer (GESO) as well as the Gain Scheduling based on the ANFIS-Fluent method. Based on these new methodologies and their findings, an excellent global flight dynamics and controller model were developed and resulted in the development of an excellent flight simulator model for both UAS-S4 and UAS-S45. This simulator model could be further generalized for other Unmanned Aerial Systems for their successful design and development.
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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 | 0.000 |
| 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".