HVAC Control in the New Millennium.pdf - HVAC.Amickracing

Artificial Intelligence, Fuzzy Logic and ControlThis is similar to PID control where changes in process dynamics alsorequire retuning.Model Predictive ControlThis type of modeling is often called time series or convolutionmodeling. It represents the open-loop process response as a vector. Asingle, unity input step change in the manipulated variable is used toaffect the output. The input behavior is represented as a sequence ofsteps and the output as the cumulative effects of those steps.The desired behavior for the output may be a path of return tosetpoint. It can be expressed as a vector and the necessary sequence ofmanipulated variable values can be calculated from the model inverse.If the process has dead time, elements of the process model vectorbecome zero. Another problem is that the residual values of the processmodel vector will affect the output. This can result in a steady-stateoffset. The methods for handling these problems involve using a filter toremove measurement noise from the model mismatch signal.The filtered value is used as a reference bias which eliminates thezeros and offset. These controllers require that the process fit a linearand stationary model.Process Model-based ControlModels can be developed from material and energy balances andthermodynamic relations. These models can be used for economic optimization.Simple, reduced, or tendency models can be used. A modelmay be represented asdxdt = f(x,m,p)wherex = outputm = manipulated variablep = an adjustable process parameter©2001 by The Fairmont Press, Inc. All rights reserved.