Methods of Trajectory Tracking for Flexible Link Manipulators
Bibliographic record
Abstract
reviewed to compare their structure and performance and to identify the most effective in resolving the difficulties. 6, 7, 8, 9, 10 The operational problems with robots in space relate to several factors. One of the most important is the structural flexibility problem of the manipulator and subsequently significant difficulties with its control systems, especially, position control. This paper presents a review of previous work on advanced control schemes for manipulator endpoint positioning while tracking a square trajectory 12.6 m x 12.6 m. by a two-link flexible robot. Schemes include; command shaping, inverse dynamics, linear quadratic regulator, linear quadratic Gaussian with linearized Kalman filter, extended Kalman filter and fuzzy logic adaptive extended Kalman filter, fuzzy logic and repetitive learning. While each scheme tracks with differing degrees of precision, fuzzy logic, in some form within a control scheme, tracks consistently with a high degree of precision, flexibility and robustness. They include; inverse dynamics (IDC), linear quadratic regulator (LQR), linear quadratic Gaussian (LQG) with linearized Kalman filter, extended Kalman filter (EKF) and fuzzy logic adaptive extended Kalman filter (FLAEKF), fuzzy logic (FLC) and, repeated learning (RL). A description of a command shaping, inverse kinematics technique for tracking a square trajectory studied by Banerjee and Singhose is included for comparison. 3, 4 Their work stimulated the studies reviewed in this paper and is alluded to in previous papers. 6, 7, 8, 9, 10
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.008 |
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".