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

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<strong>HVAC</strong> <strong>Control</strong> <strong>in</strong> <strong>the</strong> <strong>New</strong> <strong>Millennium</strong>The logical product of a fuzzy logic function <strong>in</strong> response to a specificset of <strong>in</strong>puts always occurs at a po<strong>in</strong>t <strong>in</strong> time. These calculations aresimilar to <strong>the</strong> scan cycle of a PLC.The logical sum comb<strong>in</strong>es <strong>the</strong> results of <strong>the</strong> rules. The fuzzy devicedoes not make a quick decision. The logical sum may suggest that <strong>the</strong>controlled parameter should be decreased, but whe<strong>the</strong>r this should bedone quickly or slowly is left fuzzy unless <strong>the</strong> result is TRUE or FALSE.This is sometimes called <strong>the</strong> conclusion of <strong>the</strong> fuzzy <strong>in</strong>terference.DefuzzificationThe defuzzification operation calculates <strong>the</strong> center of gravity of <strong>the</strong>fuzzy <strong>in</strong>terference. This value becomes <strong>the</strong> output. When <strong>the</strong> output isa s<strong>in</strong>gle value determ<strong>in</strong><strong>in</strong>g <strong>the</strong> required change, <strong>the</strong> defuzzification isknown as <strong>the</strong> Mamdani method and is <strong>the</strong> most common techniqueused.For multivariable control, <strong>the</strong> f<strong>in</strong>al control action is calculated from<strong>the</strong> weighted <strong>in</strong>dividual membership values of each active rule.Based on <strong>the</strong> rules programmed <strong>in</strong>to <strong>the</strong> fuzzy controller, it willoutput <strong>the</strong> most valid value correspond<strong>in</strong>g to <strong>the</strong> variable <strong>in</strong>put conditions.With fuzzy control, <strong>the</strong> steps are performed cont<strong>in</strong>uously, while <strong>in</strong><strong>in</strong>formation process<strong>in</strong>g, such procedures are executed only when <strong>the</strong><strong>in</strong>put data varies. In one fuzzy control system application <strong>the</strong> variability<strong>in</strong> product properties was reduced by about 30%.Temperature Overshoot <strong>Control</strong>A fuzzy logic controller can reduce <strong>the</strong> temperature overshoot thatoccurs dur<strong>in</strong>g heat-up. The ideal control system might start with a lowsetpo<strong>in</strong>t and <strong>the</strong>n gradually adjust it toward <strong>the</strong> correct setpo<strong>in</strong>t whilewatch<strong>in</strong>g <strong>the</strong> control performance. Fuzzy logic can be used for this typeof control (Figure 6-8).Autotun<strong>in</strong>g is used to obta<strong>in</strong> <strong>the</strong> PID constants and <strong>the</strong> process characteristicssuch as <strong>the</strong> dead time and time constant. The controller manipulates<strong>the</strong> heat <strong>in</strong>put based on <strong>the</strong> deviation from <strong>the</strong> setpo<strong>in</strong>t. It automaticallychanges <strong>the</strong> <strong>in</strong>ternal setpo<strong>in</strong>t to a lower value when <strong>the</strong> anticipatorylogic predicts an overshoot. The controller cont<strong>in</strong>ues to monitor©2001 by The Fairmont Press, Inc. All rights reserved.

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