1. Control performance assessment in the presence of valve stiction Wei Yu, David Wilson & Brent Young Industrial Information & Control Centre New Zealand diwilson@aut.ac.nz

2. Question for the day? What do you do if your control loop looks like this …? Re-tune the loop, Service the valve, or just ignore it Controller plant Characteristic triangular wave

3. Context & rationale Many control loops are badly tuned  We are lazy  Conditions change  Valves get sticky Should I retune or service the valve ?  Retuning is easier, service requires a shutdown  What is my expected economic return?  Calculate a CPA

4. What is valve stiction ? Vague term meaning valve problems Stiction: sticky/friction Typically valve sticks & jumps

5. Various levels of stiction

6. Plant under consideration Linear plant with known delay With disturbance model plant disturbance We want to control this system as best we can NL plants in CPI are smooth & benign Key NL are in actuators

7. Control Performance Assessment (CPA) Use a minimum variance controller as a performance benchmark Estimate from closed-loop data  Must know plant delay Harris Index  Zero is bad, 1 is probably “too good”, 0.7 is optimal  Calculate from disturbance model Estimate from ARIMA identification Direct from data But we need to know the deadtime, b

8. How do I estimate  2 mv with stiction ? Why bother?  Gives an indication on how good the loop would be if the valve was maintained  Is it worth shutting down & servicing the valve? How to remove the nonlinearities?  Existance of the control invariant for NL systems ( Harris & Yu )  Run a smoothing spline through the data  Identify periods when valve is stuck fast

9. Controller System under consideration (With stiction) plant Valve stiction Nonlinear phenomena disturbance measurements Un-observable

10. Our scheme Fit a non-parametric spline curve to y  OK for non-differential nonlinearities Remove nonlinearity with spline  Adjust smoothing to “just remove” NL  Use linearity check Compute  2 MV from d sequence

11. Removing the nonlinearity Fit a smooth curve to approximate z Reconstruct d from y-z Unobservable, but smoothish

12. Establishing linearity & Gaussianity The power spectrum A(f) and bispectrum B(f 1,f 2 ) of this series are The squared bicoherence is

13. Controllerplant Valve stiction disturbance measurements Example: A plant with a sticky valve

14. Testing the method Increasing level of smoothness Statistical tests

15. Monte-Carlo Simulation results True value Estimation gets worse

16. Waiting for steady-state Select periods at steady-state Use only this data for the identification

17. Areas of long periods “Islands” of long periods Increasing noise We are interested in the long periods when we might reach steady state

18. Uninteresting area Period < 10  Period = 10  contour Optimum noise level Valve too “jumpy” Too much noise, so valve is continually “dancing”

19. Does it work? True value Uncertainty bounds Good estimation due to longer sequences Bad estimation due to excessive nonlinearities

20. Conclusions Estimate the CPA even with stiction nonlinearities  Do we need to shut down? Heuristic curve smoothing is OK Extracting steady-state is better provided:  Sequences are long enough  System is stable and relatively short time constants Need to know:  Approximate process deadtime  Approximate dominant time constants Now we know if it is worthwhile to service the valve.