A Reaction-Based Stabilizer for Nonmodel-Based Vibration Control of Cable-Driven Parallel Robots
Bibliographic record
Abstract
Considering a simplified model of cables is an essential assumption in the design of state-estimator and vibration control of cable-driven parallel robots (CDPRs). Such an assumption however, impacts the effectiveness of controllers and state-estimators in such systems. This article presents a reaction-based stabilizer for nonmodel-based vibration control of CDPRs to address such model dependence. It is proved analytically that by using only three actuators and not involving the cable-connected winches, the proposed stabilizer regulates all undesired vibrations of the platform. It is also shown that the proposed stabilizer needs only the directly measurable position and velocity of its actuators to form its closed-loop control feedback signals. Effectiveness of the proposed system is more significant in CDPRs with considerable nonlinear effects of cables, where such effects are computationally costly to model, such that the performance of real-time controllers and state-estimators are spoiled. To provide a case study, a multibody nonlinear dynamic model of a suspended CDPR equipped-with the proposed stabilizer with zero cable damping effects is developed in SimMechanics/MATLAB, where a fine-tuned proportional-derivative controller is applied on each actuator to suppress all vibrations of the system. In addition, a suspended CDPR with extremely stretchable nonlinear-stiffness elastomer cables is fabricated and tested, where performance of the proposed stabilizer to suppress the high-amplitude nonlinear oscillations of the whole system is demonstrated.
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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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".