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Intelligent PID Product Design


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Presentation give by Terry Blevins at the IFAC PID'12 conference in Brescia, Italy on March 28th, 2012. Presentation based on paper by Willy Wojsznis, Terry Blevins, John Caldwell, and Mark Nixon

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Intelligent PID Product Design

  1. 1. Intelligent PID Product Design IFAC Conference on Advances in PID Control Brescia, 28-30 March 2012 Willy Wojsznis Terry Blevins John Caldwell Peter Wojsznis Mark NixonSlide 1 IFAC - PID’12 – Brescia Italy
  2. 2. What is Intelligent PID? An intelligent control system has the ability to  Improve control performance automatically or direct user to make changes that improve performance  Detect and diagnose faults and impaired loop operation  Learn about process, disturbances and operating conditions Collection of simple features improves product functionality and makes it easy to useSlide 2 IFAC - PID’12 – Brescia Italy
  3. 3. PID controller options selectionSlide 3 IFAC - PID’12 – Brescia Italy
  4. 4. Nonlinear PID parametersSlide 4 IFAC - PID’12 – Brescia Italy
  5. 5. PIDPlus for Wireless Communications  To provide best control when a measurement is not updated on periodic basis, the PID must be modified to reflect the reset contribute for the expected process response since the last measurement update.  Standard feature of the PID in DeltaV for example PIDPlus DesignSlide 5 IFAC - PID’12 – Brescia Italy
  6. 6. PID at saturated conditions A better response to major upsets can be achieved through the use of a dynamic pre-load and reducing the filtering that is applied in the positive feedback path when the output limitedSlide 6 IFAC - PID’12 – Brescia Italy
  7. 7. Performance Monitoring- OverviewSlide 7 IFAC - PID’12 – Brescia Italy
  8. 8. Performance Monitoring - Summary Identifies Loops to Tune And Tuning Values AutomaticallySlide 8 IFAC - PID’12 – Brescia Italy
  9. 9. Performance monitoring Slq + s 0.0 = Best Possible Variability Index = 100 1- 100 = Worst Possible Stot + s where: n  (X ) Total 2 Minimum 2 - X  S cap  i Standard Variance Slq = Scap 2 -   Stot = i =1 n -1 Deviation Control  S tot  n  (X i - X i -1 )2 i= 2 Best possible “capability” is Scap = s - Sensitivity Factor minimum variability 2 (n - 1 )Slide 9 IFAC - PID’12 – Brescia Italy
  10. 10. Valve Diagnostics • The approach uses the process model gain and is the best suited for the adaptive control loops or automatically tuned loops where process gain is known • Valve stem position availability improves the diagnostics • After calculating oscillation amplitudes on the controller input and output, valve HYSTERESIS is defined directly as: h = 2 A(out ) 2 Ampl ( PV ) = Kr r = 2 Ampl ( PV ) b = h-r KSlide 10 IFAC - PID’12 – Brescia Italy
  11. 11. Tuning Index  Tuning index is defined as the ratio of the potential residual PID variability reduction to the actual PID residual variability  Provides absolute benchmark based on process model and desired response  More meaningful measure than the Harris index which is based on minimum varianceSlide 11 IFAC - PID’12 – Brescia Italy
  12. 12. PID Auto-Tuning and Adaptive User InterfaceSlide 12 IFAC - PID’12 – Brescia Italy
  13. 13. PID Graphical Gain-Phase Margin TuningSlide 13 IFAC - PID’12 – Brescia Italy
  14. 14. Adaptive PID Principle  For a first order Multiple Model Interpolation with re- Estimated Gain, time plus deadtime centering constant, and process, twenty deadtime seven (27) models are evaluated each Ke -TD sub-iteration, first gain is determined, 1 + s then dead time, Changing First Order Plus process input Deadtime Process and last time constant. G1+ Δ G1+ Δ G1+ Δ  After each iteration, TC1 -Δ TC1–Δ TC1 -Δ G1 G1 DT1- Δ DT1 G1 DT1+ Δ the bank of models TC1 -Δ TC1–Δ TC1 -Δ G1-Δ DT1- Δ ΔDT1 Δ G1- G1- DT1+ Δ is re-centered using TC1 -Δ TC1–Δ TC1 -ΔΔ G1+ Δ DT1- Δ DT1 G1+ Δ G1+ TC1 DT1+ Δ TC1 TC1 the new gain, time G1 DT1- Δ DT1 G1DT1+ Δ TC1 G1 TC1 TC1 constant, and dead G1-ΔDT1-G1- Δ G1- ΔDT1+ Δ Δ DT1 time TC1 TC1 Δ TC1 Δ G1+ Δ G1+ G1+ DT1- Δ DT1 +Δ TC1+Δ TC1 +Δ TC1 DT1+ Δ G1 DT1- Δ DT1 G1 DT1+ Δ G1 TC1 +Δ TC1+Δ TC1 +Δ G1-ΔDT1-G1- Δ G1- ΔDT1+ Δ Δ DT1 TC1 +Δ TC1+Δ TC1 +Δ DT1- Δ DT1 DT1+ ΔSlide 14 IFAC - PID’12 – Brescia Italy
  15. 15. Adaptive modeling with parameter interpolation •Every parameter value of the model is evaluated independently •The weight assigned to the parameter value is inverse of the squared error •Adapted parameter value is weighted average of all evaluated values – decrease the number of models dramatically •Interpolation delivers improved accuracy, compared to selection from the limited number of modelsSlide 15 IFAC - PID’12 – Brescia Italy
  16. 16. Sequential Parameter Interpolation •Sequential parameter adaptation – less models: Model with 3 parameters (Gain, Lag, Dead Time) and 3 values for every 3 parameter has 3 model variations for model switching adaptation or 3x3 model variations for sequential parameter adaptation •Using the original data and Gain performing adaptation iteratively Dead time Initial model • 3 1 Final The procedure on-line practically 2 model feasible with sequential adaptation LagSlide 16 IFAC - PID’12 – Brescia Italy
  17. 17. Adaptive PID Diagram with model switching and parameters interpolation Controller Adaptation Models re-tuning Supervisor Evaluation i ˆ yi d Feedforward Parameter Set of - control Interpolation Models Excitation y Generator SP - PID u PV Controller + + ProcessSlide 17 IFAC - PID’12 – Brescia Italy
  18. 18. Adaptive modeling and controlSlide 18 IFAC - PID’12 – Brescia Italy
  19. 19. Adaptive model schedulingSlide 19 IFAC - PID’12 – Brescia Italy
  20. 20. Conclusions  The PID intelligence is commonly accepted by users with various level of control expertise  The main factor that contributed to the intelligent PID acceptance is robust process model identification  A significant factor is friendly user interface that provides full insight into control loop operation, control performance, loop faults and tuning recommendations  Evolution of PID design will continue. PID will be facing more challenges and deliver more successes.Slide 20 IFAC - PID’12 – Brescia Italy
  21. 21. Acknowledgments •Our communication with professors Karl Åstrӧm, Dale Seborg and Thomas Edgar greatly improved the product concepts and design. •The final shape of the product and its quality is the result of contributions from many control software developers –just to name the core of the group: Dennis Stevenson, John Gudaz, Peter Wojsznis, Mike Ott, Yan Zhang and Ron Ottenbacher.Slide 21 IFAC - PID’12 – Brescia Italy