Addressing Closed-Chain Dynamics for High-Precision Control of Hydraulic Cylinder Actuated Manipulators
Bibliographic record
Abstract
Nonlinear model-based (NMB) control methods have been shown (both in theory and in practice) to provide the most advanced control performance for highly nonlinear hydraulic manipulators. In these methods, the inverse dynamics of a system are used to proactively generate the system actuation forces from the desired motion dynamics. To model the inverse dynamics in articulated systems, the Lagrange dynamics and the Newton-Euler dynamics are the most common methods. In hydraulic cylinder actuated manipulators, a linear motion of the cylinder can be converted to a rotational joint motion between two links, creating closed-chain structures in the system. In Lagrange-dynamics-based control methods, the closed-chain structures are typically treated as an open-chain structure, which may raise the question of inaccurate system modeling. Contrary, the virtual decomposition control (VDC) approach is the first rigorous NMB control method to take full advantage of Newton-Euler dynamics, allowing to address the system nonlinear dynamics without imposing additional approximations. In VDC, the actuated closed-chain structures can be virtually decomposed to open chain structures. To address the dynamics between the decomposed open chains, three specific terms (namely two load distribution factors and an internal force vector) need to be addressed. However, analytical solutions for these terms cannot be found in the literature. This paper provides the detailed solutions for these terms, which are further needed in a high-precision control of hydraulic robotic manipulators.
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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 | 0.001 | 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 | 0.001 |
| 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".