Cooperative fuzzy model predictive control for heating and cooling of buildings

This paper presents a cooperative fuzzy model predictive control (CFMPC) for heating and cooling of buildings. Because of different supply zones, the large time constants and the non-linear building dynamics with respect to the different seasons, a CFMPC concept is proposed. The overall non-linear building is split into different zones, which are consisting of input-coupled Takagi-Sugeno (TS)-fuzzy models. Each such TS -fuzzy model is constituted by a local linear model network (LLMN). The LLMN consists of local linear models (LLM), which are representing the different seasons: winter, transition season (fall and spring), and summer. The control of each LLM is realized by model predictive control (MPC). For each building zone the associated MPCs are output-blended by the fuzzy membership functions, which leads to fuzzy model predictive control (FMPC). In addition to the FMPCs a global MPC is controlling the thermally activated building systems, which affect all other zones. To coordinate the different controllers a cooperative iteration-loop is assumed, which leads to cooperative fuzzy model predictive control (CFMPC). The concept is developed for a specific demonstration building and can be easily adapted for other complex buildings. A simulation example demonstrates that the proposed CFMPC achieves a performance increase with less energy consumption, as compared to FMPC controllers and historical measured data of the demonstration building. (C) 2015 Elsevier B.V. All rights reserved.