PID to Model Predictive Control
Yurong Kimberly Wang
Adjunct Professor - OIT
Standards
Certification
Education & Training
Publishing
Conferences & Exhibits
Objectives
• Industrial process control challenge
• PID control limitation
• Model predictive control development procedures
• MPC for operation advantage
• MPC vendors and reference materials
Tyco Electronics / Precision Interconnect
Coax Manufacturing Processes
• Dielectric layer
– Taping or Extrusion
• Shield layer
– Braiding or Serving
• Jacket layer
– Taping or Extrusion
Coax Property
• Formulae
16 .95 k
C
ln( D )
d
Zo 
59.96 D
ln( )
d
k
Td  1.016 k
•
•
•
•
•
•
C: capacitance (pF/Foot)
Td: time delay (ns/Foot)
Z0: impedance (Ohm)
k: dielectric constant
D: outer diameter (Mil)
d: center conductor diameter
(Mil)
Process Control Challenge
• Multiple Outputs
– Capacitance, Diameter, Time delay, Impedance, …
• Multiple Inputs
– Screw speed, line speed, barrel temperatures, tape tensions,
…
• Long and Variable Time Delays
– Variable line speeds and sensor to actuator distances
• Input and Output Constraints
– Input and output upper and lower spec limits
• Nonlinearity
– Variety of operating conditions
• Disturbances
– Center conductor variation, tape thickness variation, …
PID Control Limitation
•
•
•
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•
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Multiple-loop PID with decoupling
Cascade PID loops
Gain scheduling PID
Anti-windup for input constraints
Difficult to control large time delay processes
Difficult to control non-minimum phase processes
Model Predictive Control (MPC)
•
•
•
•
•
Model-based multi-variable control
Optimal control law with I/O constraints
Nonlinear control with model mismatch
Long and variable time delay processes
Non-minimum phase processes
MPC System and Optimization
MPC Sampling Instants
Tuning parameters: prediction horizon and control horizon
Process Modeling Tools
• Models based on first principals
– Mechanics, thermodynamics, heat transfer, fluid dynamics, …
– S or Z domain or state space models
• Models based on system identification
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–
–
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Step response method: TF, FIR, BJ, ARX, ARMAX,…
PRBS method: TF, FIR, BJ, ARX, ARMAX,…
MatLAB System Identification Toolbox
LabVIEW System Identification Toolkit
Simulation Tools
• MatLAB and Simulink
• LabVIEW Control Design and Simulation Module
• Tuning parameters
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–
–
–
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Output weights: the higher the weight, the closer the output to setpoint
Input weights: the higher the weight, the closer the input to setpoint
Input change weights: the higher the weight, the slower the response
Predictive horizon: up to plant settling time
Control horizon: case specific for each control objective
PID and MPC Setpoint Following
Comparison of MPC and PID with Smaller Time Delay
0.7
PVPID
PVMPC
SP
0.6
Case 1: time delay is 41 steps
y 0.0025  41

z
u
z  0.35
0.5
0.4
0.3
0.2
0.1
0
0
50
100
150
200
PID scrap
MPC scrap
250
300
350
400
450
PID and MPC Setpoint Following
Comparison of MPC and PID with Larger Time Delay
0.9
PVPID
PVMPC
SP
0.8
Case 2: time delay is 82 steps
y  0.0025 z 82
u
z  0.35
0.7
0.6
0.5
0.4
0.3
0.2
Same tuning parameters
0.1
0
0
100
200
300
400
500
600
700
800
900
MPC with Predictive SP
Comparison of MPC with Predictive SP and PID
0.6
PVPID
PVMPC
SP
0.5
Case 3: setpoint profile is used
y
0.0025  41

z
u
z  0.35
0.4
0.3
0.2
0.1
0
-0.1
0
50
100
150
200
250
300
350
400
450
LabVIEW MPC System Architecture
Quality
Engineers
Plant
Managers
Process
Engineers
Productio
n
Productio
n
Productio
n
Manufacturing
Engineers
Productio
n
Report Program for Data Analysis
Business Network
Internet
Manufacturing Information Server
Remote
Users
Productio
n
OPC Client & Server for Data Logging
Business Network
LabVIEW
HMI & MPC Control
OPC Client & Server for Data Sharing
Productio
n
Control Network
Local Control
Module
Local Control
Module
Local Control
Module
Figure 1. System Architecture
Local Control
Module
LabVIEWTM MPC Project
LabVIEW MPC Block Diagram
MatLABTM MPC Block Diagram
LabVIEW MPC Application
MPC for Operation Advantage
• Six Sigma process performance and optimal product quality control
– Multi-variable auto-controlled product quality with constraints
• Productivity improvement
– Unmanned auto production overnight run and throughput ramp up
• Equipment cost reduction
– Inner diameter gauge elimination
• Sensor fault detection
– Controller acting up with sensor fault readings
• Labor cost reduction
– Coax off-line test and operator/machine ratio reduction
MPC Vendors
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Aspen Technology
Honeywell
Emerson Process Management
Siemens
Shell Global
MathWorks
National Instruments
…
Reference Material
•
A survey of industrial model predictive control technology, Control
Engineering Practice, 2003 by S. J. Qin, and T. A. Badgwell
•
Advanced Control Unleashed, ISA, 2003 by Terrence L. Blevins,
Gregory K. McMillan, Willy K. Wojsznis, and Michael W. Brown
•
LabVIEW Model Predictive Control Module User Manual, 2009 by
National Instruments
•
MatLAB Model Predictive Control Toolbox User Manual, 2009 by
MathWorks
•
MatLAB System Identification Toolbox User manual, 2009 by
MathWorks
Conclusion
• MPC for complicated process controls
• MPC development procedures
• MPC for operation advantage
• MPC vendors and reference materials
Question and Answer
• Contact information
yurongwang1@yahoo.com
• “A survey of industrial model predictive control technology”
website:
http://cepac.cheme.cmu.edu/pasilectures/darciodolak/Review_
article_2.pdf
• “Advanced Control Unleashed” website:
http://www.isa.org/Template.cfm?Section=Books1&template
=Ecommerce/FileDisplay.cfm&ProductID=6087&file=Adv
.ControlUnleashed_TableofContents.pdf