Full-order sliding mode control of underwater flexible manipulators with echo state network disturbance compensation
Bibliographic record
Abstract
In this article, a full-order sliding mode controller with echo state network (ESN-FOSMC) disturbance compensation is proposed for the trajectory tracking and vibration suppression of underwater flexible manipulators (UFM). To improve the robustness under lumped disturbances and reduce the computational complexity of traditional recurrent neural networks method, ESN, a continuous recurrent neural network, is used to approximate and compensate model uncertainties and hydrodynamic disturbances. A FOSMC is designed to ensure accurate tracking of joints and end-effectors, and proportional-derivative (PD) control method is utilized to further suppress flexible vibration. The adaptive law of ESN is formulated by using Lyapunov method, integrating the ESN method with sliding mode control method. Then, Lyapunov method is used to prove the stability of the control system. Finally, the virtual prototype system of the UFM is established to validate the effectiveness of the proposed control method. Simulation results present that, compared with the nonsingular fast terminal sliding mode controller and a FOSMC with radical basis functions (RBF-FOSMC) neural network disturbance compensation, the ESN-FOSMC achieves superior tracking accuracy with reduced vibration.
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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) | 0.001 | 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 | 0.001 |
| 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".