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Can You Help Resolve Our PID Troubles?
We build turnkey, PLC-based batching and blending systems for the wet chemical producing industry. As customers’ requirements become more demanding, we're finding it increasingly harder to control some loops with basic PID. We’re aware of advanced options such as fuzzy logic and adaptive control, but mostly in the context of much larger operations. Does anyone have some experiences to share?
— From September 2005 CONTROL DESIGN
Stick with PID
It’s possible to control most processes using PID controllers, and PLCs are perfectly capable of handling most PID loops. However, the control engineer must be very careful when configuring the PID algorithm. In most PLCs, you’re faced with configuring about 40 parameters to properly configure a PID loop. It’s been my experience that more than 90% of PLC-based PID Loops are not properly configured. The most common errors include:
If you’re confident that the PID block is properly configured, the next step is to confirm proper tuning of the PID loop. This certainly will include performing some bump tests, and tuning, using a scientific approach. A good PID tuning package such as our PID Loop Optimizer will ensure the selection of optimal tuning parameters.
Some batch processes must handle non-linear measurements (pH, for example) or non-linear process responses. Again, these can be handled easily in a PLC with the proper design. Gather data from process bumps, define the non-linearity, and then build a characterizer to correct for it. Characterizers can be built easily in most PLCs by using a set of X-Y pairs. A sample characterizer is shown in Figure 1 below.
|FIGURE 1: CHARACTER(IZER) BUILDING|
|A characterizer can help a PLC correct for non-linear measurements.|
Except for large, complex, interacting systems, such as distillation columns, it’s rare to find a loop that PID can’t handle. Solving these problems with PID is far less costly than the training, design and implementation cost of more complex technology such as fuzzy logic.
George Buckbee, product development director, ExperTune, Hartland, Wis.
WE RECOMMEND that you use Model-Free Adaptive (MFA) control. MFA is an adaptive control method that doesn’t require process models. Therefore, it’s easy to install, use, and maintain. Once installed, no controller parameter tuning is required.
Since you have to deal with batch changes and product changeovers, PIDs might need frequent tuning. If you use a fuzzy or model-based approach, you might still need to build rules and models. Also, maintaining these rules and models can be a headache.
Why is MFA better? MFA isn’t just one controller. There are a number of MFA controllers readily available. You can simply choose the one that fits your application, do some simple configuration, and launch it. These MFAs include:
A chapter on MFA is included in the newly published Instrument Engineers’ Handbook—Process Control and Optimization by Béla Lipták, CRC Press LLC, October 2005. You can buy the book from the ISA book store at www.isa.org/books or via www.Amazon.com.
George Cheng, CTO, CyboSoft, Rancho Cordova, Calif.
BASIC PID has the property that the gain settings for the P, I and D portions are constant, regardless of the magnitude of error or change in error.
In microphones, for example, we often have automatic gain control. For a large signal, the gain is small, and for a small signal, the gain is high. If a speaker talks loudly, he will not overdrive the microphone, and yet when he whispers, he can still be heard.
Similarly, many PID controllers work better if the gain is different for large signals and for small signals. For small signals, a smaller P gain often is used. Also, the D portion is often zero, especially for small signals. This then helps to stabilize and quiet the system near zero error.
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