Robust Control of an HVAC System via a Super-Twisting Sliding Mode Technique
Bibliographic record
Abstract
This paper presents design and implementation of a super twisting sliding mode control for superheat temperature and evaporating temperature of refrigerant fluid in an evaporator of HVAC (Heating-Ventilation and Air Conditioning)-Refrigeration system. Based on a nonlinear model of the evaporator two control approaches are presented. The first approach is based on a Multi-Input Multi Output (MIMO) system in which there are two control inputs; inlet mass flow and outlet mass flow rate, and the outputs are the length of two phase flow and evaporating temperature of refrigerant. The second approach considers the system as a Single input single output (SISO) one, and by using inlet mass flow, superheat temperature is controlled. In the first approach, by implementing a feedback linearization method the two control inputs are decoupled. By decoupling the effects of both inputs, the two state variables of system are controlled separately and effectively. By applying sliding mode control robustness against the disturbances and uncertainties is guaranteed. Super-twisting algorithm is applied as a remedy for chattering problem in classical sliding mode control and achieving finite time convergence. Controller and model of systems are simulated using MATLAB and Simulink. The results of simulations show the effectiveness of designed controller in presence of uncertainties.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".