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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 Nixon
Slide 1
IFAC - PID’12 - Brescia Italy
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 use
Slide 2
IFAC - PID’12 - Brescia Italy
PID controller options selection
Slide 3
IFAC - PID’12 - Brescia Italy
Nonlinear PID parameters
Slide 4
IFAC - PID’12 - Brescia Italy
PIDPlus for Wireless Communications


Slide 5
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
IFAC - PID’12 - Brescia Italy
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
Performance Monitoring- Overview
Slide 7
IFAC - PID’12 - Brescia Italy
Performance Monitoring - Summary
Identifies Loops to Tune
And Tuning Values
Automatically
Slide 8
IFAC - PID’12 - Brescia Italy
Performance monitoring
0.0 = Best Possible
100 = Worst Possible
Slq + s
Variability Index = 100 1
- Stot +s
where:
n
Minimum
Variance
Control
S
2
lq
=
S
Scap
2-
cap
Stot =
(X
i
-X
i=1
Stot
n - 1
)
2
Total
Standard
Deviation
n
(X i
Scap =
Slide 9
i=2
- X
i-1
2 (n - 1 )
)2
Best possible “capability” is
minimum variability
IFAC - PID’12 - Brescia Italy
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:
2 Ampl(PV ) = Kr
h=2A(out)
2
Ampl(PV
)
r=
K
Slide 10
b=h-r
IFAC - PID’12 - Brescia Italy
Tuning Index
Slide 11

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 variance
IFAC - PID’12 - Brescia Italy
PID Auto-Tuning and Adaptive
User Interface
Slide 12
IFAC - PID’12 - Brescia Italy
PID Graphical Gain-Phase Margin Tuning
Slide 13
IFAC - PID’12 - Brescia Italy
Adaptive PID Principle

Multiple Model
Interpolation with recentering
Estimated
Gain, time
constant, and
deadtime
Ke-TD
1+s
Changing
process input
First Order Plus
Deadtime Process
G1+ Δ
G1+ Δ G1+ Δ
-Δ
G1
G1
G1
Δ
-Δ
G1-Δ
G1- Δ G1- Δ
+Δ
Δ
TC1 -Δ TC1-Δ TC1 -Δ
DT1- Δ DT1
DT1+ Δ
Δ
G1-Δ
TC1
DT1- Δ
G1- Δ
TC1
DT1
G1- Δ
TC1
DT1+ Δ
G1-Δ
G1- Δ G1- Δ
TC1 +ΔTC1+ΔTC1 +Δ
DT1- Δ DT1
DT1+ Δ
Slide 14
+Δ
1 +Δ
+Δ
Δ
+Δ
+Δ
IFAC - PID’12 - Brescia Italy

For a first order
plus deadtime
process, twenty
seven (27) models
are evaluated each
sub-iteration, first
gain is determined,
then dead time,
and last time
constant.
After each iteration,
the bank of models
is re-centered using
the new gain, time
constant, and dead
time
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 models
Slide 15
IFAC - PID’12 - Brescia Italy
Sequential Parameter Interpolation
•Sequential parameter adaptation - less models:
Model with 3 parameters (Gain, Lag, Dead Time) and 3 values for every
parameter has 33 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
•
The procedure on-line practically
feasible with sequential adaptation
1
3
2
Final
model
Lag
Slide 16
IFAC - PID’12 - Brescia Italy
Adaptive PID Diagram with model
switching and parameters interpolation
Adaptation
Supervisor
Controller
re-tuning
Models
Evaluation
i
d
Parameter
Interpolation
Feedforward
control
Set of
Models
Slide 17
-
y
Excitation
Generator
SP -
ŷi
PID
Controller
+
+
u
IFAC - PID’12 - Brescia Italy
Process
PV
Adaptive modeling and control
Slide 18
IFAC - PID’12 - Brescia Italy
Adaptive model scheduling
Slide 19
IFAC - PID’12 - Brescia Italy
Conclusions




Slide 20
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.
IFAC - PID’12 - Brescia Italy
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