Study of system parameters and control design for a flexible manipulator using piezoelectric transducers
Bibliographic record
Abstract
In this paper, a nonlinear control scheme is presented to achieve small tracking errors in a 2-DOF flexible manipulator.A secondary actuation mechanism using piezoelectric materials is added to the system for suppressing residual vibrations at the end point of the flexible link.A small piece of piezoceramic is also used, as a sensor, in order to obtain the modal states of the system.The effects of changing physical parameters such as relative thickness of the piezoelectric ceramic with respect to the flexible link, the optimum location and the length of the actuator are studied based on the singular value decomposition of the controllability Grammian of the system.It is shown that for each of the aforementioned parameters, an optimum value can be found which maximizes the singular value associated with one vibration mode.A partial feedback linearization technique based on output redefinition is utilized to obtain an appropriate control output for each joint and the piezoelectric actuator.A model for friction is obtained and included in the control law.Experimental results show that applying the suggested control scheme results in smooth and precise motion of the flexible manipulator without exciting unwanted vibration modes.Comparisons are made when a linear control scheme is used for the tracking problem.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".