The thesis addresses the development and analysis of model predictive control (MPC) of the HVAC system. The use of MPC enables meeting stricter requirements present in the pharmaceutical industry. The thesis presents the HVAC system and the corresponding nonlinear model. The given nonlinear model enabled the testing and development of various model predictive control methods, aimed at ensuring precise and energy-efficient control of temperature and humidity.
Review of literature confirms the suitability of using MPC for controlling HVAC systems. MPC has received much attention in various studies, as it enables relatively easy implementation of different constraints and requirements in system control. The advantage of MPC also lies in its suitability for controlling nonlinear multivariable systems.
We ensured the fulfillment of the given requirements by selecting an appropriate objective function used in optimization, which determines future control signals. The basic MPC algorithm ensures tracking of reference values for temperature and humidity. Energy-saving MPC (EMPC) was implemented by allowing deviations from the reference values but only within permitted intervals. Linearization of the nonlinear model at different operating points enabled the use of a set of linear models, which are used in predictive functional control (PFC). The advantage of PFC is the use of analytical algebraic calculations and avoidance of computationally demanding optimization algorithms.
The adequacy of each strategy was evaluated by simulating the controlled system in the MATLAB environment. Real measurements of external conditions were used in the simulation. The results allow comparison of the accuracy and efficiency of the presented developed control methods.
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