Direct Model Reference Adaptive Control of a Flexible Joint Robot
Bibliographic record
Abstract
Flexible effects in the joints of large space robots make their real-time operation a challenging task, especially when accurate endpoint positioning is required. The problem is further aggravated when the flexible joint stiffness matrix is not well known. This paper discusses the application of a model reference adaptive control (MRAC) composite system for tracking the endpoint of a flexible joint space robotic manipulator. The composite control scheme consists in a flexible control term designed to damp the joint vibrations plus a Transpose Jacobian rigid control term for which the control gains are adapted using a novel direct MRAC adaptation law. Numerical simulations show that the adaptive composite controller can maintain stability and good tracking performance despite significant uncertainties in the joint stiffness coefficients. Nomenclature ) , ( q q C & r = rigid centrifugal and Coriolis matrix x e , y e = model reference endpoint position error, ref x – x and ref y – y i I = inertia of link i , n i , , 1 K = m J = motor inertia matrix i m J = inertia of motor i , n
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 | 0.000 |
| 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 | 0.000 | 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".