Delta v fuzzy

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DeltaV Product Data Sheet
September 2009 – Page 1

DeltaV Fuzzy

DeltaV Fuzzy

Fuzzy logic loops outperform PID on non-linear and noisy process loops.

 Beats traditional PID for tolerating noisy signals and non-linear process response  Surpasses PID control for both setpoint and load changes  Offers PID ease of use and configuration  Easily tuned with DeltaV InSight

Inparticular, process loops that can benefit from a nonlinear control response are excellent candidates for fuzzy control. Since fuzzy logic provides fast response times with virtually no overshoot, it is excellent for loops that experience frequent setpoint changes or load disturbances. In addition, loops with noisy process signals have better stability and tighter control when fuzzy logic control isapplied. With DeltaV InSight software, you can easily tune DeltaV Fuzzy function blocks, for improved process quality, throughput, and efficiency.

The DeltaV Fuzzy function block offers a practical, fieldproven substitute for PID control. The patented algorithm provides faster, tighter response and superior performance over traditional PID control on most loops. It is even capable ofhandling loops where manual control was once the only option.

DeltaV Product Data Sheet
September 2009 – Page 2

DeltaV Fuzzy
Tests on DeltaV Fuzzy have shown loop performance improvement of 30-40% over traditional PID. These improvements come from large reductions in overshoot and decreased settling or response time. You don’t need to understand fuzzy logicrules. The rules are set within the controller. You only manipulate scaling factors. Furthermore, the optional DeltaV InSight product is capable of tuning fuzzy loops by adjusting these scaling factors. These scaling factors are analogous to the proportional, integral, and derivative factors of a traditional PID loop. Fuzzy logic takes more aggressive control action on large control errors (PV notequal to SP) than small ones. It also takes aggressive action to eliminate oscillatory behavior. This is accomplished by balancing high gain response on large errors with strong damping when the loop becomes oscillatory: the result—tighter, more precise control. The figures on the following page compare the fuzzy logic response curve to PID control with various tuning settings. First-order processmodel with dead time was used to compare fuzzy logic control performance to traditional PID control. The following figure shows the response curve for a PID loop tuned for aggressive response. This almost always results in significant overshoot. The fuzzy logic response was equally aggressive but did not overshoot.

Beats PID for tolerating noisy signals and nonlinear processresponse. Until now, the only way to
manage noisy process signals was to filter out the noise resulting in sluggish control. The non-linear response curve makes fuzzy logic capable of handling noisy signals while maintaining stable responsive control. It even minimizes overshoot at the same time. The fuzzy logic control function blocks supplied with DeltaV Fuzzy can improve performance on a wide range ofprocess loops, even those difficult-to-control loops that require a non-linear control response curve. Fuzzy control handles them with ease.

Surpasses PID control for both setpoint and load changes. Fuzzy logic recovers faster from error between
setpoint and the process variable than even aggressively tuned PID loops. Since it returns the process to setpoint faster and with little-to-noovershoot, fuzzy control is excellent for loops where the setpoint changes often or for loops that experience frequent process load disturbance. Temperature and composition loops where overshoot can ruin the product also benefit from fuzzy logic control’s response curve.

Offers PID ease of use and configuration. You
don’t have to learn how to adjust the ―fuzzy rules‖. This is the big difference...
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