Auto Tunning PI Controller with Reinforcement Learning Applied to Continuous Microalgae Culture
Bibliographic record
Abstract
Continuous microalgae cultures are nonlinear, time-varying bioprocesses. To regulate the output of interest (biomass) by manipulating the control variable (dilution rate), researchers have proposed numerous control strategies capable of withstanding parameter variations and disturbances. This work adopts reinforcement learning (RL) to auto tune a PI controller under changing internal and external plant conditions. A policy gradient agent with a neural network containing an LSTM layer was trained online in a simulated nonlinear environment. Twentyfive random seeds were evaluated to estimate the statistical robustness of the learning process. The plant model combines Monod and Contois sub models for a continuous culture of Chlorella vulgaris. The best agent delivered consistent tracking and control in nominal and perturbed scenarios, outperforming a conventional PI controller under similar conditions. These findings indicate that RL based tuning is a viable strategy for improving the robustness and adaptability of bioprocess control systems.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".