Control of a flexible-joint robot using a stable adaptive introspective CMAC
Bibliographic record
Abstract
This paper proposes an adaptive control for a rigidlink, flexible-joint robot using the Cerebellar Model Articulation Controller (CMAC) and the backstepping method, which is a suitable method when joints are underdamped and exhibit a large amount of flexibility. A previously proposed robust weight update method, deemed the introspective method, is placed into a Lyapunov-stable framework. In the introspective method, each local CMAC cell measures the output error in its own domain and over the domain of several sequentially activated cells on the same CMAC array. The cell then votes on whether it appears its previous weight update has reduced this error or not. The sum of all votes from the activated cells determines whether weight updates continue. In order to ensure uniformly ultimately bounded signals, a robust CMAC operates in parallel using a conservative e-modification weight update. Simulations with a two link flexible-joint arm show significantly improved performance over e-modification and a model-based LQR control.
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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 | 0.001 |
| Scholarly communication | 0.001 | 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".