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Distributed Robust Model Predictive Control for Multi-zone Air Conditioning Systems

Azim Mezerji, Hojat | 2022

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 55512 (08)
  4. University: Sharif University of Technology
  5. Department: Mechanical Engineering
  6. Advisor(s): Moradi, Hamed
  7. Abstract:
  8. Optimizing the energy consumption of air conditioning systems has gained great importance in recent years. In addition, maintaining thermal comfort and air quality in buildings is important. The multi-evaporator air conditioning system, which is a direct expansion system, shows the complex and distributed structure of air conditioning systems due to the thermal connection between zones and its large scale. The modeling of these systems is associated with uncertainties. Therefore, designing a suitable control structure for multi-zone systems is very important to achieve optimal energy consumption and maintain comfort conditions in the presence of uncertainty. Model predictive control has been introduced as one of the most successful control methods in air conditioning systems. And distributed control is a structure that has suitable performance for multi-zone air conditioning systems.The research that we presented is a hierarchical robust model predictive control approach for a single-zone direct expansion air conditioning system and in a distributed manner in multi-zone system. The objectives include minimizing peak demand and energy costs, reducing communication resources, computational complexity, while maintaining thermal comfort and indoor air quality in acceptable range. Also, appropriate and robust performance of the control approach is expected in the presence of model uncertainties. This control method consists of two layers. The upper layer is an open-loop optimizer that collects local measurement information of the single-zone and multi-zone system to establish the reference points of the single-zone system controller and the multi-zone system controllers in the lower layer by optimizing the energy demand and costs under the time-of-use rate structure while maintaining the thermal comfort and indoor air quality within the comfort range. On the other hand, for the single-zone system in the lower layer, the robust model predictive controller is designed, which is responsible for tracking the trajectory references calculated by the upper layer, as well as robust performance in the presence of uncertainty. Also for the multi-zone system in the lower layer, distributed robust model predictive controllers are designed in parallel and by sharing information between neighboring zones, which is responsible for tracking the trajectory references calculated for each region by the upper layer, as well as robust performance in presence of uncertainties in the model. Simulation results are presented to demonstrate the advantages of the designed control approaches
  9. Keywords:
  10. Thermal Comfort ; Robust Control ; Indoor Quality ; Distributed Model Predictive Control ; Multi-Evaporator Air Conditioning System ; Energy Consumption Optimization

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