An adaptive backstepping robust control method of water hydraulic high-speed on/off valve considering nonlinear hydrodynamic force
Bibliographic record
Abstract
Water hydraulic high-speed on/off valve (HSV) direct-driven by a voice coil motor (VCM) is extensively utilized in water hydraulic systems for underwater manipulators due to its simplistic structure, compact dimensions, rapid switching speed, and cost-effectiveness. In this paper, a model-based nonlinear adaptive backward robust control (ABRC) strategy is proposed to compensate for the nonlinear hydrodynamic forces and uncertain parameters of the HSV spool. A state-space equation using an equivalent model of HSV to perform ABRC design is developed, which considers valve spool disturbance and system parameter uncertainty. The dynamic response and displacement tracking accuracy of the valve spool under the ABRC algorithm through joint simulation analysis in AMESim/Simulink are investigated in comparison with Fuzzy-PID and sliding mode control (SMC) algorithms. The experimental results demonstrate that ABRC outperforms both the Fuzzy-PID and SMC algorithms in terms of dynamic response, with a spool opening time of approximately 5.2 ms and a closing time of around 6.0 ms. In addition, the RMSE values of ABRC under different operating conditions are 0.00064, 0.00247, and 0.00732, respectively. These results demonstrate significantly improved accuracy in tracking spool displacements compared to traditional control methods. Therefore, the BRAC strategy exhibits superior adaptability and robustness across varying operating conditions relative to conventional control methods. The findings of this study will provide valuable guidance for the design and engineering implementation of water-hydraulic HSV control systems in underwater manipulators.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".