A comparative study of H<sub>∞</sub> and PID control for indirect deformable object manipulation
Bibliographic record
Abstract
Manipulation of deformable objects is a field of research that has a variety of applications, including medical procedures, food industry, and manufacturing. This paper compares the performance of a standard PID controller and a robust H∞controller, both of which are designed to perform the task of indirect deformable object manipulation. The H∞controller is generated using methods outlined by Doyle et al., and the PID controller is tuned in a conventional trial and error approach. Unmodelled dynamics of the deformable object system are considered disturbance inputs and compensated by both controllers. Loop shaping is used to model the robotic actuators and position feedback system limitations within the process of generating the H∞controller. The H∞proved to be superior to the PID in both simulations and experiments. Maximal steady state error of the H∞controller was 0.5mm; PID maximal error was 1 mm at steady state. The key benefit of the H∞approach is that the controller generated using the numerical model of the deformable object performed well in simulations and experiments; the parameters of the PID controller required retuning between simulation and the experimental setting.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".