Discrete-Time Adaptive Neural Tracking Control and Its Experiments for Quadrotor Unmanned Aerial Vehicle Systems
Bibliographic record
Abstract
For the control of a quadrotor unmanned aerial vehicle, the strong nonlinearities, coupling, and underactuated problem in both positioning and attitude systems of the quadrotor are major challenging issues to be solved. In this article, a discrete-time adaptive dynamic surface control (DSC) scheme for the quadrotor is proposed to obtain a satisfactory tracking performance. The nonlinearities and couplings are overcome by employing the designed robust adaptive DSC nonlinear control method. The underactuated problem is overcome by solving the designed adaptive neural control equations. Also, different from the continuous-time control scheme, the discrete-time control is more suitable for the computer and network control in practicable cases. Furthermore, the digital first-order low-pass filters are constructed to predict the future virtual control signal in the backstepping method, leading to the avoidance of the model transformation problem in the discrete-time backstepping method.
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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 | 0.001 |
| 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) | 0.001 | 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".