Regulation of soil moisture using zone model predictive control
Bibliographic record
Abstract
This paper concerns the input-output model identification and zone model predictive control of an agro-hydrological system modeled by a partial differential equation. The primary control objective is to maintain the soil moisture within a desired range which is suitable for grass grow. There is also a secondary control objective which is to reduce the total irrigation amount. First, a linear parameter varying (LPV) model is identified for controller design purpose using a maximum likelihood gradient-based iterative estimation method. Then, based on the LPV model, a zone model predictive control (MPC) is designed which uses an output disturbance and state observer to reduce model-plant mismatch and an asymmetric target zone to reduce irrigation amount under weather uncertainties while maintaining the soil moisture within the target range. Simulation studies show that the LPV model is a good approximation of the original nonlinear model and effectively reduces the online computational load of the MPC, and that the proposed zone MPC can lead to significant water conservation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".