An Improved Gap Metric and Stability Margin-Based Analysis to Control Heating, Ventilating, and Air Conditioning Systems Based on Multiple-Model and Model Order Reduction
محورهای موضوعی : مهندسی هوشمند برق
Pouya Rikhtehgar
1
(
Department of Mechanical, Computer, and Electrical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
)
Mohammad Haeri
2
(
EE, Sharif U. of T.
)
کلید واژه: air quality, Heating, ventilating, and air conditioning (HVAC) system, Indoor environment, Outdoor environment, Model order reduction, Multiple models,
چکیده مقاله :
To address computational complexity in heating, ventilating, and air conditioning systems, two general reduced multiple model control designs based on gap metric, stability margin, and model order reduction are proposed. The difference between two designs lies in the sequence of implementing model order reduction and multiple model techniques, resulting in distinct control approaches. As the number and location of reduced multiple models are not necessarily the same in two cases, the selected models will also be different. This could make one approach preferable to another in terms of closed-loop performance. Therefore, we introduce a model selection criterion to predict the most suitable approach for improving indoor thermal comfort and air quality in considered system. This criterion is based on maximum gap metric, maximum stability margin, and number of nominal models. Finally, two new approaches called OR-MM and MM-OR and a new criterion called MSC are proposed. To validate the effectiveness of our method, we conduct computer simulations that demonstrate their achievements.
To address computational complexity in heating, ventilating, and air conditioning systems, two general reduced multiple model control designs based on gap metric, stability margin, and model order reduction are proposed. The difference between two designs lies in the sequence of implementing model order reduction and multiple model techniques, resulting in distinct control approaches. As the number and location of reduced multiple models are not necessarily the same in two cases, the selected models will also be different. This could make one approach preferable to another in terms of closed-loop performance. Therefore, we introduce a model selection criterion to predict the most suitable approach for improving indoor thermal comfort and air quality in considered system. This criterion is based on maximum gap metric, maximum stability margin, and number of nominal models. Finally, two new approaches called OR-MM and MM-OR and a new criterion called MSC are proposed. To validate the effectiveness of our method, we conduct computer simulations that demonstrate their achievements.
