Energy requirements for heating and cooling of buildings constitute a major fraction of end use energy consumed. Therefore, it is important to provide the occupant comfort requirements in buildings in an energy efficient manner. However, buildings are large scale complex systems, susceptible to sensor, actuator or communication network failures in their thermal control infrastructure, that can affect their performance in terms of occupant comfort and energy efficiency. The degree of decentralization in the control architecture determines a fundamental tradeoff between performance and robustness. This thesis studies the problem of thermal control of buildings from the perspective of partitioning them into clusters for decentralized control, to balance underlying performance and robustness requirements. Measures of deviation in performance and robustness between centralized and decentralized architectures in the Model Predictive Control framework are derived. Appropriate clustering algorithms are then proposed to determine decentralized control architectures which provide a satisfactory trade-off between the underlying performance and robustness objectives. Two different partitioning methodologies – the CLF-MCS method and the OLF-FPM method – are developed and compared. The problem of decentralized control design based on the architectures obtained using these methodologies is also considered. It entails the use of decentralized extended state observers to address the issue of unavailability of unknown states and disturbances in the system. The potential use of the proposed control architecture selection and decentralized control design methodologies is demonstrated in simulation on a real world multi-zone building.
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