ADAPTATION OF CONTROL MODELS FOR VENTILATION SYSTEMS
DOI:
https://doi.org/10.32782/IT/2024-2-5Keywords:
modeling, ventilation system, control, bilinear model, Lyapunov function, error functionAbstract
Optimizing the operation of automated ventilation and air conditioning systems using information about the current state of indoor air parameters allows for rapid adaptation to changing external conditions, improves building energy efficiency, and enhances human comfort. Therefore, the development of modern control technologies for such systems, based on adequate mathematical models, is a relevant task. Research Objective. The aim of this research is to develop a control model for a ventilation system that ensures compliance with regulatory air quality indicators, based on measured data about the current state of indoor air. Methodology. The research methodology involves using a discrete dynamic model and bilinear systems theory to describe the integrated control model of the ventilation system. This approach allows for the consideration of the relationship between the system state and the control influence. Two approaches are applied for adapting the parameters of the control model: using the Lyapunov function stability condition and minimizing the error function, which is calculated as the difference between the observed output of the system and the modeled output. The corresponding algorithms for model parameter adaptation–gradient descent and the least squares method–are provided. Scientific Novelty. The scientific novelty lies in comparing two approaches to adapting the control model of the ventilation system. Conclusions. It was established that adapting the model by minimizing the error function between the observed and modeled system output is preferable due to its focus on model accuracy, ease of implementation, computational efficiency, flexibility, and adaptability. Obtaining information about the real state of the air from sensors and identifying the model parameters allows for the control system model to be adaptive and robust. Adaptive control of the ventilation system based on accurate air parameter data allows for reduced energy consumption.
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