Adaptive neural fault-tolerant control for output-constrained attitude tracking of unmanned space vehicles
Bibliographic record
Abstract
The challenging problem of attitude tracking control for unmanned space vehicles (USVs) subject to actuator faults and output constraints is addressed in this study. A novel adaptive neural fault-tolerant controller is proposed by integrating the neural networks (NNs) and barrier Lyapunov function (BLF) with the backstepping technique. Two NNs are adopted to approximate the uncertain nonlinear terms caused by unknown attitude dynamics and actuator faults, respectively. Moreover, the BLF is introduced to tackle the output constraints. It is strictly proved that all the closed-loop error signals are uniformly ultimately bounded under the proposed controller. Totally, the proposed adaptive neural fault-tolerant controller has the following two distinctive features. (1) The proposed controller is model-free and can still be applicable even when the USV attitude dynamic model is completely unknown in advance. (2) The proposed controller can guarantee the attitude tracking error always within the predefined output constraints even in the presence of actuator faults and thus ensuring safety. Finally, the excellent tracking performance of the proposed controller is verified through numerical simulations and comparisons.
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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 | 0.001 | 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".