Adaptive super twisting sliding mode control of a HVAC system
Bibliographic record
Abstract
In this paper an adaptive super twisting sliding mode cascaded control strategy to control superheat temperature of an evaporator of Heating Ventilation Air Conditioning Systems(HVAC) is presented. Two internal loop and external loop of the cascaded controller are designed using sliding mode by utilizing feedback linearization method. By controlling superheat temperature, Tsh, in the external loop, and evaporating temperature of refrigerant, Te, in the internal loop, a better performance with robustness against parameter uncertainty is achieved. The value of superheat temperature is determined by using the estimated value of length of two phase flow of the refrigerant inside the evaporator. The performance of the proposed control strategy against disturbance and parameter uncertainties is illustrated through simulation in MATLAB/Simulink environment. It is shown that in comparison with super-twisting method, the proposed adaptive super twisting method improves the performance of system by reduction of undesirable chattering in the response of system.
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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 | 0.000 |
| 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) | 0.001 | 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".