Simplified Fuzzy Model Based Predictive Control for a Nonlinear System

[Abstract] A reduced complexity fuzzy model has been developed to capture the nonlinear dynamics of a mechanical system. The use of Functional Principal Analysis to reduce the complexity of the model permitted the use of a linear controller based on that modelThe authors gratefully acknowledge the Spanish Ministry of Economy and Competitivenes for its financial support of part of this work through the grant DPI2013-46912-C2-1https://doi.org/10.17979/spudc.978849749808

Estimation of Large-Scale Multi-Rate Systems ⋆

model predictive control and estimation of large-scal

Application of distributed model predictive approaches to temperature and CO\u3csub\u3e2\u3c/sub\u3e concentration control in buildings

\u3cp\u3eIn the context of energy consumption reduction, this paper focuses on the application of Model Predictive Control to occupants’ thermal comfort together with indoor air quality control while improving the whole building energy efficiency First, an open-space office split in three zones, located in Cork Institute of Technology, is modeled. A centralized MPC is designed to control the temperature and CO2 concentration in the three zones. Then, a distributed version of the MPC, with three separate local controllers, is considered. Finally, simulation results show that the distributed MPC solution achieves control performance quite close to the centralized version with less computing effort.\u3c/p\u3

Application of Distributed Model Predictive Approaches to Temperature and CO2CO_2 Concentration Control in Buildings

International audienceIn the context of energy consumption reduction, this paper focuses on the application of Model Predictive Control to occupants' thermal comfort together with indoor air quality control while improving the whole building energy efficiency. First, an open-space office split in three zones, located in Cork Institute of Technology, is modeled. A centralized MPC is designed to control the temperature and $CO_2$ concentration in the three zones. Then, a distributed version of the MPC, with three separate local controllers, is considered. Finally, simulation results show that the distributed MPC solution achieves control performance quite close to the centralized version with less computing effort