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PID to Model Predictive Control

Yurong Kimberly Wang

Adjunct Professor - OIT

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

)ln(96.59

d

D

kZo

• C: capacitance (pF/Foot)• Td: time delay (ns/Foot)

• Z0: impedance (Ohm)

• k: dielectric constant• D: outer diameter (Mil)• d: center conductor diameter

(Mil)

• Formulae

)ln(

95.16

dDk

C

kTd 016.1

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

• 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– 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– 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

0 50 100 150 200 250 300 350 400 4500

0.1

0.2

0.3

0.4

0.5

0.6

0.7Comparison of MPC and PID with Smaller Time Delay

PVPID

PVMPCSP

Case 1: time delay is 41 steps

4135.0

0025.0

zzu

y

PID scrap

MPC scrap

PID and MPC Setpoint Following

0 100 200 300 400 500 600 700 800 9000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9Comparison of MPC and PID with Larger Time Delay

PVPID

PVMPCSP

Same tuning parameters

Case 2: time delay is 82 steps

8235.0

0025.0

zzu

y

MPC with Predictive SP

0 50 100 150 200 250 300 350 400 450-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6Comparison of MPC with Predictive SP and PID

PVPID

PVMPCSP

Case 3: setpoint profile is used

4135.0

0025.0

zzu

y

LabVIEW MPC System Architecture

Production

QualityEngineers

Production

Plant Managers

Production

ProcessEngineers

Production

Manufacturing Engineers

Manufacturing Information Server

Business NetworkReport Program for Data Analysis

Production

RemoteUsersInternet

Control Network

Local Control Module

Local Control Module

Local Control Module

Business Network

OPC Client & Server for Data Logging

OPC Client & Server for Data Sharing

Production

LabVIEWHMI & MPC Control

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

• 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 informationyurongwang1@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