New adaptive iterative learning control (AILC) for uncertain robot manipulators
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Bibliographic record
Abstract
In this paper, we propose two simple adaptive iterative learning control (AILC) algorithms for trajectory tracking control problem of rigid robot manipulators that track the same control trajectory repeatedly over a finite time interval. The design comprises of a linear parameterization robot feedback control structure and a learning parametric adaptation law that iteratively updates unknown uncertain parameters based upon the use of a Lyapunov energy function. In contrast to other existing adaptive ILC schemes for robot manipulators, where large feedback and learning gains are required to get robustness against large modelling uncertainties and disturbances in the early stage of the operation, the proposed adaptive ILC schemes require small feedback gains. The presented scheme 2 is simpler in structure and easier to implement in the real-world operation in the sense that it requires less computational effort and computing power without any priori knowledge of robot dynamics. Owing to the robustness of the adaptation laws against large disturbances and modelling uncertainties in the early trials, a high-learning gain can be used in order to achieve fast learning convergence.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it