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Chapter 15. PID Controllers Applied to MIMO Processes. 2×2 Example of a MIMO Process. Example of a 2×2 MIMO Process. Two inputs: Setpoints for flow controller on steam and reflux. Two outputs: Composition of products B and D. Configuration Selection (Choosing the u/y Pairings) .

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Chapter 15 l.jpg

Chapter 15

PID Controllers Applied to MIMO Processes



Example of a 2 2 mimo process l.jpg
Example of a 2×2 MIMO Process

  • Two inputs: Setpoints for flow controller on steam and reflux.

  • Two outputs: Composition of products B and D


Configuration selection choosing the u y pairings l.jpg
Configuration Selection (Choosing the u/y Pairings)

  • That is, which manipulated variable is to be used to control which controlled variable.

  • Choosing an inferior configuration can dramatically reduce control performance.

  • For many processes, configuration selection is a difficult and challenging process (e.g., dual composition control for distillation).



Example of single loop pid controllers applied to 2 2 process l.jpg
Example of Single Loop PID Controllers Applied to 2×2 Process

  • L is adjusted by PID controller to maintain composition of D at its setpoint.

  • Steam flow is adjusted by PID controller to maintain composition of B at its setpoint.



Example of coupling l.jpg
Example of Coupling Process

  • L is adjusted to maintain the composition of D which causes changes in the composition of B.

  • The bottom loop changes the flow rate of steam to correct for the effect of the reflux changes which causes changes in the composition of D.


The three factors that affect configuration selection l.jpg
The Three Factors that Affect Configuration Selection Process

  • Coupling

  • Dynamic response

  • Sensitivity to Disturbances



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Relative Gain Array Process

  • When l11 is equal to unity, no coupling is present.

  • When l11 is greater than unity, coupling works in the opposite direction as the primary effect.

  • When l11 is less than unity, coupling works in the same direction as the primary effect.


Numerator of l 11 l.jpg
Numerator of Processl11


Denominator of l 11 l.jpg
Denominator of Processl11


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RGA Example Process


D c 1 1 0 d y 2 k 21 d c 1 0 05 l.jpg
D Processc1 = 1.0Dy2 = K21Dc1 = 0.05


D c 2 d y 2 k 22 0 05 2 0 025 l.jpg
D Processc2 = -Dy2/K22 = -0.05/2 =-0.025


D y 1 coup d c 2 k 12 0 025 0 1 0025 l.jpg
( ProcessDy1)coup = Dc2 K12 = -0.025(0.1) =-.0025




Rga analysis l.jpg
RGA Analysis Process

  • RGA is a good measure of the coupling effect of a configuration if all the input/output relationships have the same general dynamic behavior.

  • Otherwise, it can be misleading.



Dynamic example l.jpg
Dynamic Example Process

  • Note that the off-diagonal terms possess dynamics that are 10 times faster than the diagonal terms.

  • As a result, adjustments in c1 to correct y1 result in changes in y2 long before y1 can be corrected. Then the other control loop makes adjustments in c2 to correct y2, but y1 changes long before y2. Thus adjustments in c1 cause changes in y1 from the coupling long before the direct effect.



Dynamic rga l.jpg
Dynamic RGA Process


Dynamic rga for direct a and reverse b pairings l.jpg
Dynamic RGA for Direct (a) and Reverse (b) Pairings Process

  • Consider the frequency, w, corresponding to desired closed loop response which indicates b better than a


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Overall Dynamic Considerations Process

  • Pairings of manipulate and controlled variables should be done so that each controlled variable responds as quickly as possible to changes in its manipulated variable.


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Sensitivity to Disturbances Process

  • In general, each configuration has a different sensitivity to a disturbance. Note that thick and thin line represent the results for different configurations


Configuration selection l.jpg
Configuration Selection Process

  • It is the combined effect of coupling, dynamic response, and sensitivity to disturbances that determines the control performance for a particular control configuration for a MIMO process.





Configuration selection example l.jpg
Configuration Selection Example Process

  • L, L/D, and V are the least sensitive to feed composition disturbances.

  • L and V have the most immediate effect on the product compositions followed by L/D and V/B with D and B yielding the slowest response.



Analysis of configuration selection example l.jpg
Analysis of Configuration Selection Example Process

  • Note that (L,V) is the worst configuration in spite of the fact that it is the least susceptible to disturbances and the fastest acting configuration, but it is the most coupled.

  • Even though (D,V) had an RGA of 0.06, it had decent control performance.

  • (L,B) is best since it has good decoupling and the overhead product is most important.


Tuning decentralized controllers l.jpg
Tuning Decentralized Controllers Process

  • When a particular loop is 3 times or more faster than the rest of the loops, tune it first.

  • When tuning two or more loop with similar dynamics, use ATV identification with online tuning



Overview l.jpg
Overview Process

  • The combined effect of coupling, sensitivity to disturbances, and dynamic response determine the performance of a configuration

  • Implement tuning of fast loops first and use a single tuning factor when several loops are tuned together.

  • One-way decoupling can be effective when the most important controlled variable suffers from significant coupling.


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