Using CMAC for adaptive nonlinear MPC and optimal setpoint identification of an activated sludge process
Why this work is in the frame
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Bibliographic record
Abstract
This paper proposes both an adaptive nonlinear model predictive control and a method to identify an optimal setpoint. Local discrete-time linear models, estimated from output measurements, are stored in a Cerebellar Model Arithmetic Computer (CMAC). The CMAC provides a practical way to store, access, and interpolate the models in real-time and for future-time predictions. A finite-horizon nonlinear optimization decides on a desired control signal for training a CMAC controller. In order to search for on an optimal setpoint in the case of a measured disturbance, another set of local linear models is produced that depends on only outputs and disturbances. A Lyapunov-based method ensures stability (uniformly ultimately bounded signals) in the cases of a cart-pendulum system and an activated sludge process for wastewater treatment. Simulation results show successful trajectory tracking and setpoint identification for both systems in simulation.
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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 | 0.001 |
| 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 it