control performance assessment in the presence of valve stiction
DESCRIPTION
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. Question for the day? . What do you do if your control loop looks like this …?. Characteristic triangular wave. - PowerPoint PPT PresentationTRANSCRIPT
Control performance assessment
in the presence of valve stiction
Wei Yu, David Wilson & Brent YoungIndustrial Information & Control CentreNew Zealand
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
-0.5
0
0.5
1
1.5
outp
ut
0 100 200 300 400 500 600 7000
0.2
0.4
0.6
0.8
time
Con
trolle
r Inp
ut
uuv
Characteristic triangular wave
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
What is valve stiction ? Vague term meaning valve problems Stiction: sticky/friction Typically valve sticks & jumps
Various levels of stiction
Plant under considerationLinear 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
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
How do I estimate s2mv 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
+
Controller
System under consideration(With stiction)
+
plant
Valve stictionNonlinear phenomena
disturbance
measurements
Un-observable
Our scheme
Fit a non-parametric spline curve to yOK for non-differential nonlinearities
Remove nonlinearity with splineAdjust smoothing to “just remove” NLUse linearity check
Compute s2MV from d sequence
Removing the nonlinearity Fit a smooth curve to approximate z Reconstruct d from y-z
Unobservable, but smoothish
Establishing linearity & Gaussianity The power spectrum A(f) and bispectrum B(f1,f2) of this
series are
The squared bicoherence is
+
Controller
+
plantValve stiction
disturbance
measurements
Example: A plant with a sticky valve
Testing the method
Increasing level of smoothness
100 150 200 250 300 350 4000.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
Time
Out
put
y=0.1=0.05=0.01
Statistical tests
Monte-Carlo Simulation results
True value
Estimation gets worse
Waiting for steady-state Select periods at steady-state Use only this data for the identification
Areas of long periods
“Islands” of long periods
Increasing noise
We are interested in the long periods when we might reach steady state
Uninteresting areaPeriod < 10
Period = 10 contour
Optimum noise level
Valve too “jumpy”
Too much noise, so valve is
continually “dancing”
Does it work?
True value
Uncertainty bounds
Good estimation due to longer sequences
Bad estimation due to excessive nonlinearities
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.