Finding a near-optimal neural-adaptive control solution without increasing the training time
Bibliographic record
Abstract
This paper proposes a new robust modification for weight update laws in direct adaptive control using the Cerebellar Model Articulation Controller (CMAC) that will quickly find a near-optimal control. The method uses two sets of supervisory weights, identified using cost functionals as the best found so far in the training, to guide the online weight updates in a supervised leakage term. The first set compensates for nonlinearities along the desired trajectory, acting like a feedforward component. Once the error has been driven close to the origin, additional supervisory weights seek an optimal control that penalizes extra control effort and error about the origin, providing a near-optimal feedback component. The algorithm to identify these best-weights-so-far takes advantage of the local nature of the CMAC basis function domains i.e. local cost functionals correspond to a particular weight. A Lyapunov analysis guarantees uniformly ultimately bounded signals. Simulations with a flexible-joint arm, where the control needs to compensate for gravity and a sinusoidal external disturbance, show that the proposed method significantly outperforms both a CMAC with trained with leakage and a model-based LQR control, without increased control effort or training time.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".