A new robust weight update for cerebellar model articulation controller adaptive control with application to transcritical organic rankine cycles
Why this work is in the frame
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Bibliographic record
Abstract
This work proposes modifications to the adaptive update law for a cerebellar model articulation controller (CMAC) and develops a model of a transcritical organic rankine cycle (ORC) to test it on. Owing to the local nature of its basis functions, the CMAC exhibits more weight drift (overlearning) than other types of neural networks, and practical applications have been restricted to systems without persistent oscillations of the inputs. The proposed solution to this problem here involves identifying a set of weights that is the best found so far in the training, and keeps the weights from drifting too far from these best weights. The method results in uniformly ultimately bounded signals, established through Lyapunov analysis. To show the improved training algorithm now allows the CMAC to control more general systems, it is applied to the control of a transcritical ORC. Part of the contribution of this paper also includes developing a model to describe the behaviour of a supercritical fluid in the ORC evaporator. The control method is compared with proportional–integral control, where the controls have to provide robustness to fluctuations and step changes in heat source temperatures.
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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