Application of the integral manifold concept for the end-effector trajectory tracking of a flexible link manipulator
Bibliographic record
Abstract
A new control strategy for the end- effector trajectory tracking (EETT) of a single flexible link manipulator (SFLM) is introduced. The linear dynamic model of the SFLM is expressed in the singularly perturbed form. To reduce the EETT error, a corrective torque is added to the "computed torque control" command of the rigid link counterpart of the SFLM. This corrective torque is derived based on the concept of the integral manifold of the singularly perturbed differential equations. It is proven that the EETT error is a function of the fundamental natural frequency of the SFLM. That is, the order of the EETT error, after employing 3 2 this new method, is greater thanε3and smaller thanε2, whereε = 1/(2πf)andfis the fundamental natural frequency of the SFLM. The implementation of the introduced technique does not require the full state measurements, since by designing an observer; the rate of the change of the flexible variables with respect to time is estimated. Thus only the measurements of the joint rotation, joint velocity, and flexible variables are required. The proof of the stability, based on the Lyapunov criteria, is given. The results of the simulation and experimental studies are also included. Making the error of the EETT smaller and reducing the number of state measurements are the main contributions of this work.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".