This paper deals with the problem of robust model predictive control of an uncertain linearized model of a building envelope and HVAC system. Uncertainty of the model is due to the imperfect predictions of internal and external heat gains of the building. The Open-Loop prediction formulation of the Robust Model Predictive Control (OL-RMPC) is known to be unnecessarily over-conservative in practice. Therefore, we adopt a Closed-Loop prediction formulation of Robust Model Predictive Control (CL-RMPC) which exploits an uncertainty feedback parameterization of the control sequence and results in a tractable formulation of the problem. To improve on the efficiency of CL-RMPC we propose a new uncertainty feedback parameterization of the control input, which leads to a number of decision variables linear in time horizon as opposed to quadratic as in previous approaches. To assess our approach we compare three different robust optimal control strategies: nominal MPC which does not have a priori information of the uncertainty, OL-RMPC and CL-RMPC. We show results from a quantitative analysis of performance of these controllers at different prediction error values of the disturbance. Simulations show that CL-RMPC provides a higher level of comfort with respect to OL-RMPC while consuming 36% less energy. Moreover, CL-RMPC maintains perfect comfort level for up to 75% error in the disturbance prediction. Finally, the newly proposed parameterization maintains the performance of CL-RMPC while reducing the simulation time by an average of 30%.
- Dynamic Systems and Control Division
Optimal Control of Building HVAC Systems in the Presence of Imperfect Predictions
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Maasoumy, M, & Sangiovanni-Vincentelli, A. "Optimal Control of Building HVAC Systems in the Presence of Imperfect Predictions." Proceedings of the ASME 2012 5th Annual Dynamic Systems and Control Conference joint with the JSME 2012 11th Motion and Vibration Conference. Volume 2: Legged Locomotion; Mechatronic Systems; Mechatronics; Mechatronics for Aquatic Environments; MEMS Control; Model Predictive Control; Modeling and Model-Based Control of Advanced IC Engines; Modeling and Simulation; Multi-Agent and Cooperative Systems; Musculoskeletal Dynamic Systems; Nano Systems; Nonlinear Systems; Nonlinear Systems and Control; Optimal Control; Pattern Recognition and Intelligent Systems; Power and Renewable Energy Systems; Powertrain Systems. Fort Lauderdale, Florida, USA. October 17–19, 2012. pp. 257-266. ASME. https://doi.org/10.1115/DSCC2012-MOVIC2012-8523
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