3D Positioning of a Stewart Platform Using Soft Pneumatic Actuators: A Design Approach
Bibliographic record
Abstract
In this paper, the application of a novel soft bellows pneumatic actuator (SBPA) into an advanced mechatronic system specifically, a Stewart platform, is investigated.Our previously research has demonstrated that the newly designed SBPA can generate forces exceeding 100 N, achieving a contraction ratio greater than 40% relative to its maximum length, and reaching motion speeds above 60 mm/s.Moreover, precise linear positioning within 10 μm has been achieved through the application of a Linear Quadratic Regulator (LQR).To further evaluate the capabilities of the developed actuator, a six-degree-of-freedom Stewart platform was designed using three identical SBPAs.The 3D positioning of the platform was evaluated under open-loop control, using a camera-based system to track the displacement of key points on the platform for model identification and validation of the control results.The developed Stewart platform achieved a positioning error of 1.1% at the centre of the platform when all SBPAs were activated.Additionally, the platform's dynamic performance was assessed by actuating the SBPA with sinusoidal inputs at varying frequencies.At 1 Hz, the platform exhibited consistent vibrational motion, indicating its potential for use in vibration-based applications.This study advances the development of SBPAs and provides insight into their integration in complex mechatronic systems.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".