Robust Control of Underwater Vehicles with Fault-Tolerant Infinity-Norm Thruster Force Allocation
Bibliographic record
Abstract
There are to objectives to this paper. First, a chattering-free sliding mode controller is proposed for the trajectory control of remotely operated vehicles (ROVs). Secondly, a new approach for thruster force allocation is proposed that is based on minimizing the linfinnorm. With regards to the former, a new adaptive term is developed that eliminates the high frequency control action inherent in a conventional sliding-mode controller, and also removes the need for a priori knowledge of upper bounds on uncertainties in the dynamic parameters of the ROV. With regards to the latter, it is demonstrated that the linfinnorm optimization can be cast as a linear problem that affords easy incorporation of the thruster saturation limits. Using numerical simulations, it is shown that the proposed linfinthruster allocation is capable of meeting the adaptive sliding mode controller's demands in the presence of thruster failures and is therefore fault tolerant. Finally, a recurrent neural network is designed in order to obtain a real time solution rate to the thruster allocation problem.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".