Intelligent PID Product Design

1. Intelligent PID Product Design IFAC Conference on Advances in PID Control Brescia, 28-30 March 2012 Willy Wojsznis Terry Blevins John Caldwell Peter WojsznisMark Nixon Slide 1 IFAC - PID’12 - Brescia Italy

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 productfunctionality and makes it easy to use Slide 2 IFAC - PID’12 - Brescia Italy

3. PID controller options selection Slide 3 IFAC - PID’12 - Brescia Italy

4. Nonlinear PID parameters Slide 4 IFAC - PID’12 - Brescia Italy

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 lastmeasurement update.  Standard feature of the PID in DeltaV for example Slide 5 IFAC - PID’12 - Brescia Italy

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 limited Slide 6 IFAC - PID’12 - Brescia Italy

7. Performance Monitoring- Overview Slide 7 IFAC - PID’12 - Brescia Italy

8. Performance Monitoring - Summary Identifies Loops to Tune And Tuning Values Automatically Slide 8 IFAC - PID’12 - Brescia Italy

9. Performance monitoring 0.0 = Best Possible100 = Worst Possible Slq + s Variability Index = 100 1 +s -Stot where: n (X -X) Total 2 MinimumVariance S  i 2 S  Standard cap = S 2- i=1 S = lq cap   tot - Deviation n 1 Control  Stot  n 2 (X - X )  i i-1 Best possible “capability” is S = i=2 cap ( - ) minimum variability 2 n 1 Slide 9 IFAC - PID’12 - Brescia Italy

10. Valve Diagnostics •The approach uses the process model gain and is the best suited forthe adaptive control loops or automatically tuned loops where processgain is known • Valve stem position availability improves the diagnostics • After calculating oscillation amplitudes on the controller input andoutput, valve HYSTERESIS is defined directly as: 2 Ampl(PV ) = Kr h=2A(out) 2 Ampl(PV ) b=h-r r= K Slide 10 IFAC - PID’12 - Brescia Italy

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 isbased on minimum variance Slide 11 IFAC - PID’12 - Brescia Italy

12. PID Auto-Tuning and AdaptiveUser Interface Slide 12 IFAC - PID’12 - Brescia Italy

13. PID Graphical Gain-Phase Margin Tuning Slide 13 IFAC - PID’12 - Brescia Italy

14. Adaptive PID Principle For a first order  plus deadtimeprocess, twenty Multiple Model Estimated Interpolation with re- Gain, time centering constant, and seven (27) modelsare evaluated eachsub-iteration, first deadtime Ke-TD gain is determined, 1+s then dead time,and last timeconstant. Changing First Order Plus process input Deadtime Process G1+ ΔG1 G1 After each iteration,the bank of models G1+ Δ G1+ Δ  -ΔG1 Δ -Δ is re-centered using Δ Δ G1-Δ G1- Δ G1- Δ + Δ the new gain, timeconstant, and deadtime TC1 -Δ TC1-Δ TC1 -Δ DT1- Δ DT1 DT1+ Δ G1-Δ G1- Δ G1- Δ + Δ Δ TC1 TC1 TC1 DT1- Δ DT1 DT1+ Δ +Δ+ Δ 1 +Δ G1-Δ G1- Δ G1- Δ + ΔTC1 +ΔTC1+ΔTC1 +Δ DT1- Δ DT1 DT1+ Δ Slide 14 IFAC - PID’12 - Brescia Italy

15. Adaptive modeling with parameter interpolation •Every parameter value of the model is evaluatedindependently •The weight assigned to the parameter value is inverse ofthe squared error •Adapted parameter value is weighted average of allevaluated values - decrease the number of modelsdramatically •Interpolation delivers improved accuracy, compared to selection from the limited number of models Slide 15 IFAC - PID’12 - Brescia Italy

16. Sequential Parameter Interpolation •Sequential parameter adaptation - less models: Model with 3 parameters (Gain, Lag, Dead Time) and 3 values for everyparameter has 33 model variations for model switching adaptation or 3x3 model variations for sequential parameter adaptation •Using the original data andperforming adaptationiteratively Gain Dead time Initialmodel • 1 3 The procedure on-line practicallyfeasible with sequential adaptation Final 2 model Lag Slide 16 IFAC - PID’12 - Brescia Italy

17. Adaptive PID Diagram with model switching and parameters interpolation Adaptation Models Controller Supervisor Evaluation re-tuning i ŷi d Feedforward Parameter Set of - Interpolation Models control Excitation y Generator u PV SP PID - + + Process Controller Slide 17 IFAC - PID’12 - Brescia Italy

18. Adaptive modeling and control Slide 18 IFAC - PID’12 - Brescia Italy

19. Adaptive model scheduling Slide 19 IFAC - PID’12 - Brescia Italy

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. Acknowledgments •Our communication with professors Karl Åstrӧm, DaleSeborg and Thomas Edgar greatly improved the product concepts and design. •The final shape of the product and its quality is the result ofcontributions from many control software developers -just to name thecore of the group: Dennis Stevenson, John Gudaz, Peter Wojsznis, Mike Ott,Yan Zhang and Ron Ottenbacher. Slide 21 IFAC - PID’12 - Brescia Italy