MULTIVARIABLE CONTROL OF SINGLE ZONE HYDRONIC HEATING-SYSTEMS WITH NEURAL NETWORKS

In a hydronic heating system, it is important to control the zone temp erature with a certain degree of accuracy. The system consists of a sp ace heating circuit with the ability to modulate both the fuel mass fl ow of the burner and the mass flow of hot water. The physical model of the system consists of a heating system, a distribution system and an environmental zone which are simulated by an eleventh order nonlinear system. The objective of the controller is to maintain the zone tempe rature as close as possible to a chosen setpoint and, at the same time , to maintain the temperature of the hot water below a maximum tempera ture which is usually 90 degrees C. However, there are many problems i n designing such a controller due to system uncertainties, saturation of the actuators and long system delay. To overcome the above nonlinea rities, an adaptive controller has been designed using neural networks and evaluated with software simulations. Comparisons are made between the RMS of the zone temperature and the energy consumption of the sys tem for a time period of two days by several neural networks with diff erent architectures and learning laws. (C) 1998 Elsevier Science Ltd. All rights reserved.