A planar parallel manipulator - dynamics revisited and controller design
Bibliographic record
Abstract
In this paper, the dynamic modelling and control design of a planar parallel manipulator used as a pick-and-place machine, is addressed. First, in a departure from standard modelling techniques utilized for planar parallel mechanisms, it is demonstrated that since the translational axes of the manipulator are driven by DC motors through industry standard ball screws, the nonlinear dynamics and coupling effects of the nonlinear dynamics of the manipulator are greatly reduced by a very large effective gear ratio factor, in this case, 1.097 xlO6. The dynamics of the driving motors thus become the dominant dynamics in the system. Hence, the dynamics of the entire system can be approximated as a set of three identical linear dynamic equations, each of which represents the dynamics of one kinematic chain, with constraints representing the coupling of these axes. Then a robust closed-loop controller designed with a Convex Integrated Design (CID) method is determined, such that multiple closed-loop performance specifications, together with a robustness specification, are simultaneously satisfied. The robustness of the closed-loop controller thus guarantees that the controller, although determined based on a simplified linear model, performs as expected on the practical system, i.e., the manipulator, hence results in satisfactory closed-loop performance. Both simulation and experiments conducted demonstrate that the multiple simultaneous closed-loop performance specifications are satisfied thus validating the simplified modeling strategy and verifying the effectiveness of the control design approach.
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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 | 0.001 |
| 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) | 0.003 | 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".