Robust sliding mode tracking control of quadrotor integrated with observer and tracking differentiator
Bibliographic record
Abstract
Abstract This paper presents a high‐frequency sliding mode control (HFSMC) approach that utilizes a nonlinear disturbance observer (NDO) and an improved tracking differentiator (TD) for achieving robust trajectory tracking control of a quadrotor unmanned aerial vehicle (UAV) in the presence of lumped disturbances and parameter uncertainties. Firstly, the quadrotor control system is decoupled into an inner‐loop subsystem focused on attitude adjustment and an outer‐loop subsystem dedicated to position control. The hierarchical control mechanism of the inner–outer loop solves the under‐actuation problem. Secondly, NDO is utilized to estimate and counteract lumped disturbances in real time, enhancing the system disturbance rejection capability. Additionally, a high‐frequency switching function is introduced into the reaching law to improve the reaching speed and handle parameter uncertainties, while the improved TD is used to smooth the desired attitude signals and their derivatives, reducing the chattering inherent in sliding mode control. This scheme effectively alleviates control input chattering while enhancing controller robustness, offering a simpler design and stronger disturbance rejection compared to nonsingular fast terminal sliding mode control (NFTSMC). Finally, the stability of the system is proven using globally uniformly ultimately bounded (GUUB) and Lyapunov theory. The effectiveness of the control strategy was validated through simulation experiments.
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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 | 0.000 |
| 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) | 0.001 | 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".