Multiple fuzzy model-based temperature predictive control for HVAC systems
In this paper, a multiple model predictive control (MMPC) strategy based on TakagiSugeno (TS) fuzzy models for temperature control of air-handling unit (AHU) in heating, ventilating, and air-conditioning (HVAC) systems is presented. The overall control system is constructed by a hierarchical two-level structure. The higher level is a fuzzy partition based on AHU operating range to schedule the fuzzy weights of local models in lower level, while the lower level is composed of a set of TS models based on the relation of manipulated inputs and system outputs correspond to the higher level. Following this divide-and-conquer strategy, the complex nonlinear AHU system is divided into a set of TS models through a fuzzy satisfactory clustering (FSC) methodology and the global system is a fuzzy integrated linear varying parameter (LPV) model. A hierarchical MMPC strategy is developed using parallel distribution compensation (PDC) method, in which different predictive controllers are designed for different TS fuzzy rules and the global controller output is integrated by the local controller outputs through their fuzzy weights. Simulation and real process testing results show that the proposed MMPC approach is effective in HVAC system control applications.
ventilating and air-conditioning ahu: air-handling unit ccu: cooling coil unit fopdt: first order plus dead time fsc: fuzzy satisfactory clustering gpc: generalized predictive control hvac: heating lpv: linear varying parameter mmpc: multiple model predictive control mpc: model predictive control pdc: parallel distribution compensation pid: proportionalintegralderivative rmse: root mean square error ts model: takagisugeno model vav: variable air volume