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

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Artificial Intelligence, Fuzzy Logic and <strong>Control</strong>Figure 6-10. Feedback with <strong>in</strong>verse, model and adjustment functionsTransfer function models use an open-loop Laplace transform descriptionof <strong>the</strong> process response to a step <strong>in</strong>put. This is a common controlmodel<strong>in</strong>g approach but is limited to l<strong>in</strong>ear and simplistic dynamicmodel<strong>in</strong>g.Time series models <strong>in</strong>volve <strong>the</strong> open-loop response of <strong>the</strong> processto a vector of impulses. These are empirically determ<strong>in</strong>ed and consist ofabout 30 elements. More precision is possible but matrix/vector algebrais required. This is <strong>the</strong> most common model<strong>in</strong>g approach used formodel-based control.Nonl<strong>in</strong>ear phenomenological models are design-based simulatorsfor nonl<strong>in</strong>ear or nonstationary processes. Their control <strong>in</strong>telligencecomes with model<strong>in</strong>g and computational complexity.Internal Mode <strong>Control</strong>This is a type of model<strong>in</strong>g that uses open-loop, step-responseLaplace transfer functions. The basic structure is shown <strong>in</strong> Figure 6-11. Asimple first-order-plus-dead-time representation is shown <strong>in</strong> Figure 6-12.©2001 by The Fairmont Press, Inc. All rights reserved.

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