9/12/2008 7:17 AM | |
Joined: 10/31/2005 Last visit: 9/4/2024 Posts: 366 Rating:
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Ronald, I'm looking forward to it... |
9/13/2008 2:39 AM | |
Joined: 8/10/2006 Last visit: 8/13/2024 Posts: 298 Rating:
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Ronald, I sincerely appreciate this primer. Can the moderators make it a sticky for a while at least? Jim |
9/16/2008 5:13 AM | |
Joined: 1/21/2008 Last visit: 7/12/2024 Posts: 39 Rating:
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Model Predictive Control (2) This is the second posting regarding this subject. And as promised, this posting will address the Process. In PID control the process typically is boiled down to a single Process Variable (or Control Variable, a CV) and one Output to the process (or Manipulated variable). A disturbance (Disturbance Variable, DV) can be introduced to add a pre-emptive function to the algorithm. In Model Predictive Control the bandwidth of the Process that is being controlled typically needs to be expanded, the built-in MPC controller that you find in PCS 7 will be able to control 4 CV's, using 4 MV's and it will support one DV. What does this mean (beside the numerical presence). This implies that a single control algorithm will control ofmultiple Temperatures, and a Pressure in a distillation column. (the CV's) It does so by manipulating up to 4 signals that in any way have impact on the process. (reboiler energy, reflux flow, pressure control) It also allows for one disturbance (uncontrolled variable). (feed flow) The number of CV's and MV's can be unrelated, you can for example control 3 CV's with 2 MV's, all MV's work together to control all CV's (there is not a one-on-one relationship like in PID control) The MV's and DV are variables that have impact on the process, the result of this impact is the CV's their value(the measurements). This is the core of the concept. Because we capture the impact of the combined activity of the MV's and DV on the process, and the resulting effects on the CV's, we can have an algorithm use that understanding to come up with a balanced control action. To be able to identify the impact of the MV's and DV, we need some mathematical tools to extract that impact and qualify/quantify it. The impact is defined for each individual MV and DV as it works on each CV. This means that, if all MV's, DV's and CV's are used in the PCS 7 MPC controller, the impact consists of 20 (5 x 4) relationships. The impact is defined as a time-based coefficient for each of these relationships. (a number that changes over time) The exercise of identifying this impact is the Modelling process, SIMATIC PCS 7 provides a tool (Modprecon) that performs this exercise, this tool is included in PCS 7 V7.0 SP1. Also if you do not intend to implement a MPC controller, the information that the tool extracts from a process response, can be very helpfull to define initial settings for a PID control algorithm. The produced data will give direction to the process gain (how strong does the process responds to a MV change) and process timing (when does the process show to be steady again). I have attached a zip file with screenshots and exports of the following process: One CV, two disturbances and one MV. Note that a MV has the same effect as a disturbance, it has impact on the process. A MV seems to be different, because it is being manipulated, but in regard to what it does to the process its impact is not qualifieddifferent from the impact of a disturbance variable. The following picturesare in the zip file 1. Processresponse.jpg, this is a screenshot of data recorded in the Trend tool of the CFC editor. It shows the CV, DV, DV2 and MV (OUT) As you can see, the CV changes somewhat, the OUT changes continuously and the DV's change stepwise. The OUT is controlled by a normal PID loop 2. The first step that the modelling tool will take is to extract the different DV's and MV's from the data. The results can be found in CV1. jpg, MV1. jpg, DV.jpg and DV2.jpg Note that MV1, DV and DV2 are impacting the process and CV1 is the result of that impact. 3. The relationship between DV and CV1, DV2 and CV1, MV1 and CV1 is being identified and displayed in MV1-CV1.jpg, DV-CV1.jpg and DV2-CV1.jpg. Note that these relationships show the positive or negative response and time (secs). A positive response implies that an increase of the DV or MV results in an increase of the CV, and that a decrease will result in a decrease. A negative response reverses this (increase results in decrease and decrease results in increase). The model is a process with two heating sources (DV and DV2) and one cooling source (MV1), CV1 is the temperature. After the relationships have been identified, the tool will use these to "Model" the response by performing a calculation and in CV1.jpg you can actually see the % match between the reality (recorded value of CV1) and the Model (calculated CV1). Once the Model (in fact the relationships) have been identified, we are half-way to a working MPC controller, however, we can see now, what gain/time response each DV and MV has on our process, as mentioned before, this is usefull for any type of control strategy. Next time we will talk about how this information is turned into a control algorithm and how that control algorithm uses it to drive up to 4 MV's. After we complete this thread, I can provide additional information to have you generate your models yourself and also sample projects with simulation. Ronald Nijssen AttachmentMPC-Model.ZIP (680 Downloads) |
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