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|Title:||Offset-free MPC: A novel design and Application to HVAC Systems|
|Keywords:||Model Predictive Control, energy efficiency, offset-free control, heat pump, vapor compression cycle|
|Abstract:||This thesis considers the problem of implementation of Model Predictive Control (MPC) strategies in the general area of Heating, Ventilation, Air Conditioning (HVAC). Specifically, the contributions utilize the constraint handling and optimality properties of MPC to achieve energy efficient control of many different HVAC systems. First, the thesis focuses on a linear offset-free MPC design for a vapor compression cycle. The key contributions include a a sequential tuning method and application to a detailed simulation test-bed, demonstrating superior closed-loop results to that of traditional control strategies in the presence of both disturbances and measurement noise. Next, a modified linear offset-free MPC formulation is implemented on a heat pump. The key contribution is the formulation of an optimization problem that recognizes the tradeoff between energy conservation and tracking performance. Simulation results illustrate superior performances as measured through three separate metrics: safety, energy efficiency and tracking. The implementation of MPC formulations to these realistic problems also pointed to a lack of MPC formulations with explicit performance considerations in the control design. Thus, in the final part of the thesis, these observed shortcomings in the standard offset-free linear MPC design are addressed via a new performance specification-based MPC. Desired closed-loop output response is specified and achieved through a tiered optimization formulation that can handle plant model mismatch. Superior closed-loop response, in terms of desired transient behavior and disturbance rejection, relative to standard linear-based and offset-free MPC designs is achieved. Finally, directions for future work are discussed.|
|Appears in Collections:||Open Access Dissertations and Theses|
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|thesis_wallaceOutta6.pdf||Matt Wallace PhD thesis||14.22 MB||Adobe PDF||View/Open|
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