Modelling and control of HVAC systems - Peh, Wei Kuan (2015)
Due to the high nonlinearities and time-varying characteristics of today’s control systems, fuzzy learning controllers have consequently find its practicality in most applications. As being a part of a research project, this report presents the method of the T-S fuzzy logic as learning mechanism that is used to acquire knowledge information for the main fuzzy-logic controller from a number of input-output sample data pairs of an unknown plant. These sample data pairs will then be divided into fuzzy clusters using the G-K clustering algorithm. After that, the coefficients of the local polynomial will be identified using least square method for each cluster. Finally, the coefficients and fuzzy clusters obtained will then be used in a Fuzzy PI controller of a centralized SISO system whereby the gains are calculated using the coefficient parameters. Having shown that fuzzy logic can be effectively used in nonlinear SISO dynamical system, it is then applied to a decentralized TITO HVAC system to maintain the variable parameters such as temperature and humidity close to the target desired values.