Active Tension-Disturbance Rejection Hybrid Control for Cable Driven Manipulator Based on Learning-Optimized Dynamic Model
Bibliographic record
Abstract
Cable-driven manipulators (CDM) offer distinct advantages in confined spaces where access by driving mechanisms is restricted. However, by virtue of the flexibility of cables in the CDM structure and the joint coupling arising from the series connection of cables, dynamic modeling of cable distribution constitutes a significant challenge, while enhancing positional accuracy represents another critical issue. To address these issues, this paper proposes an active tension-disturbance rejection hybrid control for CDM based on learning-optimized dynamic model. Within this framework, an online learning method is employed to approximate dynamic compensation terms and optimize the desired cable tension in the tension control loop. Furthermore, an extended state observer is utilized to estimate the disturbances originating from the tension loop or the external environment, with active compensation employed to enhance both the control accuracy of CDM and the robustness of the position control loop. Experimental results validate that the proposed strategy effectively reduces the dynamical modeling error while simultaneously enhancing the positioning accuracy of the position control loop.
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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) | 0.001 | 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 | 0.001 | 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".