Optimal Feedback Control for HVAC Systems: An Integral Sliding Mode Control Approach Based on Barrier Function
Bibliographic record
Abstract
Efficient control of air-handling units (AHUs) in heating, ventilating, and airconditioning (HVAC) systems is crucial for maintaining comfortable conditions while minimizing energy consumption.This study focuses on a multi-input multi-output (MIMO) control design for a nonlinear dynamic model of an AHU in a single thermal zone featuring variable air volume (VAV) properties in cooling mode.The goal is to develop decoupling controllers for the AHU by manipulating the airflow rate and cold water flow rate.An integral sliding mode control based on barrier function is proposed for regulating the humidity ratio of the thermal zone according to the desired characteristics.Subsequently, an integral sliding mode control based on barrier function is combined with an optimal feedback controller using a linear quadratic regulator (LQR) to manage indoor temperature.Additionally, an approximate classical sliding mode differentiator (ACSMD) is designed to estimate unmeasurable states that are used to construct the sliding variable of the second controller.The performance of the proposed control is evaluated through numerical simulations.Results demonstrate the ability of the controllers to guide the humidity and temperature of the thermal zone toward the required values without prior knowledge of the upper bounds on parameter variation, reducing chattering and yielding an optimal robust integral sliding mode control/LQR controller.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".