Copyright © 2014 Rockwell Automation, Inc. All Rights Reserved.
PUBLIC INFORMATION
PID Controller Tuning
Advancing the Sta...
Robert Rice, PhD
Vice President, Engineering
Control Station, Inc.
PID Controller Tuning
Advancing the State-of-the-Art wi...
Copyright © 2014 Control Station, Inc. All Rights Reserved
Outline of Discussion
Introduction to Process Control
Brief his...
Copyright © 2014 Control Station, Inc. All Rights Reserved
History of Feedback / PID Control
300BC – 1200 AD
Float Regulat...
Copyright © 2014 Control Station, Inc. All Rights Reserved
PID Tuning and Optimization
A well controlled process has less ...
Copyright © 2014 Control Station, Inc. All Rights Reserved
Process Optimization
Underperforming Controllers Can Cripple Pl...
Copyright © 2014 Control Station, Inc. All Rights Reserved
Significant Opportunity:
Uncovering the Value of Improved Contr...
Copyright © 2014 Control Station, Inc. All Rights Reserved
Steps to Controller Design and Tuning
Identify the
Controller a...
Copyright © 2014 Control Station, Inc. All Rights Reserved
Step 1: Find Controller, Specify Objective
How do you identify ...
Copyright © 2014 Control Station, Inc. All Rights Reserved
Step 1: Find Controller, Specify Objective
Good Control is “SIM...
Copyright © 2014 Control Station, Inc. All Rights Reserved
Step 1: Find Controller, Specify Objective
What is/are the prim...
Copyright © 2014 Control Station, Inc. All Rights Reserved
A bump test must generate a response
that clearly dominates the...
Copyright © 2014 Control Station, Inc. All Rights Reserved
Open loop tests require the
controller output to be stepped
Clo...
Copyright © 2014 Control Station, Inc. All Rights Reserved
Step 2: Step or Bump the Process
Bad Bump Tests
AVIOD
Disturban...
Copyright © 2014 Control Station, Inc. All Rights Reserved
Step 3: Fit a Process Model
Self-Regulating
If all inputs & out...
Copyright © 2014 Control Station, Inc. All Rights Reserved
Self-Regulating
∙
→ Process Gain
→ Time Constant [time]
→ Deadt...
Copyright © 2014 Control Station, Inc. All Rights Reserved
63%∆
∆
∆
Process Gain
 How Far
How Far does the PV Move
for Ch...
Copyright © 2014 Control Station, Inc. All Rights Reserved
Integrating Process Gain
 How Far and How Fast
How Far and How...
Copyright © 2014 Control Station, Inc. All Rights Reserved
Step 3: Fit a Process Model
By Hand or Autotune Approach Suffic...
Copyright © 2014 Control Station, Inc. All Rights Reserved
Step 4: Tune the PID Loop
1
First compute, , the closed loop ti...
Copyright © 2014 Control Station, Inc. All Rights Reserved
Step 4: Tune the PID Loop
1
First compute, , the closed loop ti...
Copyright © 2014 Control Station, Inc. All Rights Reserved
Step 4: Tune the PID Loop
1
The closed loop time constant, , sh...
Copyright © 2014 Control Station, Inc. All Rights Reserved
Step 4: Tune the PID Loop
Flow Loops
 3 to 5 times the Open Lo...
Copyright © 2014 Control Station, Inc. All Rights Reserved
Step 4: Tune the PID Loop
Set point tracking (servo) response a...
Copyright © 2014 Control Station, Inc. All Rights Reserved
Kc*2
Kc/2
Kc
Step 4: Tune the PID Loop
Challenges of PI Control...
Copyright © 2014 Control Station, Inc. All Rights Reserved
2*Kc
Kc / 2
Kc
Step 4: Tune the PID Loop
Challenges of PI Contr...
Copyright © 2014 Control Station, Inc. All Rights Reserved
Step 4: Tune the PID Loop
PID shows decreased oscillations comp...
Copyright © 2014 Control Station, Inc. All Rights Reserved
Step 5: Implement and Test Results
Testing PID Controllers Typi...
Copyright © 2014 Control Station, Inc. All Rights Reserved
Step 6: Document, Document, Document.
Who
Who is accountable
fo...
Copyright © 2014 Control Station, Inc. All Rights Reserved
Processes Have Time Varying Behavior
The CO to PV behavior desc...
Copyright © 2014 Control Station, Inc. All Rights Reserved
Example Process: Heat Exchanger
Process Variable (PV)
Set Point...
Copyright © 2014 Control Station, Inc. All Rights Reserved
Heat Exchanger Shows Nonlinear Behavior
Processes often exhibit...
Copyright © 2014 Control Station, Inc. All Rights Reserved
Controller’s Robust Stability
What does it mean for a controlle...
Copyright © 2014 Control Station, Inc. All Rights Reserved
Options For Tuning
Manual Tuning
• Time Consuming, and may not ...
Copyright © 2014 Control Station, Inc. All Rights Reserved
Full Non-Steady State Modeling
Open and Closed Loop Modeling
Su...
Copyright © 2014 Control Station, Inc. All Rights Reserved
Summary
First Order Models provide Important Information
How Fa...
Copyright © 2014 Control Station, Inc. All Rights Reserved
Questions?
Thank you for attending!
Contact Information:
Bob Ri...
Copyright © 2014 Rockwell Automation, Inc. All Rights Reserved.
We care what you think!
 On the mobile app:
1. Locate ses...
Copyright © 2014 Rockwell Automation, Inc. All Rights Reserved.
PUBLIC INFORMATION
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Control Station, Inc.: PID Controller Tuning: Advancing the State-of-the-Art with Patent-Pending Modeling

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This presentation explores the practical and economic challenges of tuning industrial PID control loops. It highlights unique capabilities for modeling highly dynamic process data and tuning for optimal controller performance. Real-world examples and application with
control loop performance monitoring included.

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Control Station, Inc.: PID Controller Tuning: Advancing the State-of-the-Art with Patent-Pending Modeling

  1. 1. Copyright © 2014 Rockwell Automation, Inc. All Rights Reserved. PUBLIC INFORMATION PID Controller Tuning Advancing the State-of-the-Art with Patent-Pending Modeling Control Station
  2. 2. Robert Rice, PhD Vice President, Engineering Control Station, Inc. PID Controller Tuning Advancing the State-of-the-Art with Patent-Pending Modeling
  3. 3. Copyright © 2014 Control Station, Inc. All Rights Reserved Outline of Discussion Introduction to Process Control Brief history of Process Control Introduction to Process Behavior and the Control Objective Why understanding the process is fundamental to controlling it The importance of stating the correct control objective PID Controller Tuning Method The PID Controller What is a PID Controller Examples of the PID controllers (e.g. PI vs PID) Theory Vs the Real-World Questions and Answers
  4. 4. Copyright © 2014 Control Station, Inc. All Rights Reserved History of Feedback / PID Control 300BC – 1200 AD Float Regulators used in Water Clocks (P-Only Control) Used a float to control the inflow of water through a valve; as the level of water fell the valve opened and replenished the reservoir. This float regulator performed the same function as the ball and cock in a modern flush toilet. 1700 – 1900 : Industrial Revolution Centrifugal (Flyball) Governors (P-Only Controller) This device employed two pivoted rotating flyballs which were flung outward by centrifugal force. As the speed of rotation increased, the flyweights swung further out and up, operating a steam flow throttling valve which slowed the engine down. Thus, a constant speed was achieved automatically. 1900 – Current : Mass Manufacturing Pneumatic, Electronic, Model Predictive Controllers PID Control
  5. 5. Copyright © 2014 Control Station, Inc. All Rights Reserved PID Tuning and Optimization A well controlled process has less variability in the measured process variable (PV), so the process can be operated close to the maximum profit constraint.
  6. 6. Copyright © 2014 Control Station, Inc. All Rights Reserved Process Optimization Underperforming Controllers Can Cripple Plant Profitability 0% 50% 100% Controllers That are Operated in Manual Mode Controllers That are Poorly Tuned or De-Tuned to Mask Other Issues Control Systems That are Not Properly Configured to Meet Their Objective 20% 30% 65%
  7. 7. Copyright © 2014 Control Station, Inc. All Rights Reserved Significant Opportunity: Uncovering the Value of Improved Control Production Throughput Production  Yield Energy Consumption Production Defects 2 – 5% 5 – 15% 25 – 50% 5 – 10% Benefits of regular PID tuning can be found across a production facility:
  8. 8. Copyright © 2014 Control Station, Inc. All Rights Reserved Steps to Controller Design and Tuning Identify the Controller and Specify the Design Level of Operation (DLO) and Control Objective Find Perform a “Bump Test” and Collect Dynamic Process Data Step Fit a Model to the Process Data Model Use Tuning Correlations to Calculate Tunings Based on Model Tune Implement and Test results Test Document the Tuning Process Document
  9. 9. Copyright © 2014 Control Station, Inc. All Rights Reserved Step 1: Find Controller, Specify Objective How do you identify PID loops that need to be retuned? Reactive: Response to the Operators Needs Proactive: Analyze Process Data to determine PID Loops that contribute to increased process variability Proactive Monitoring Should: Identify Mechanical, Process and Controller Tuning Related Issues Provide Root-Cause Detection Recommendation for Corrective Action Display Customizable Reports
  10. 10. Copyright © 2014 Control Station, Inc. All Rights Reserved Step 1: Find Controller, Specify Objective Good Control is “SIMPLE”
  11. 11. Copyright © 2014 Control Station, Inc. All Rights Reserved Step 1: Find Controller, Specify Objective What is/are the primary Control Objective(s)? Maintain Liquid Level In the Reflux Drum Maintain Column Stability Prevent Environmental Release by avoid Drum Hi Limit Reflux Drum – Level Control Example
  12. 12. Copyright © 2014 Control Station, Inc. All Rights Reserved A bump test must generate a response that clearly dominates the random (noisy) PV behavior Here the PV moves about 4 times the noise band, a good value Step 2: Step or Bump the Process Data Should Show “Cause and Effect”
  13. 13. Copyright © 2014 Control Station, Inc. All Rights Reserved Open loop tests require the controller output to be stepped Closed loop tests require a sharp controller output change Step 2: Step or Bump the Process Good Bumps Tests sharp CO movement sharp CO movement
  14. 14. Copyright © 2014 Control Station, Inc. All Rights Reserved Step 2: Step or Bump the Process Bad Bump Tests AVIOD Disturbance Driven Data & Slow Ramp CO Changes
  15. 15. Copyright © 2014 Control Station, Inc. All Rights Reserved Step 3: Fit a Process Model Self-Regulating If all inputs & outputs are held constant, the process will seek a steady-state Ex: Heat Exchanger Non Self-Regulating Process will only reach a steady-state at its ‘balancing’ point Ex: Surge Tank Types of Process Behavior
  16. 16. Copyright © 2014 Control Station, Inc. All Rights Reserved Self-Regulating ∙ → Process Gain → Time Constant [time] → Deadtime [time] Non Self-Regulating ∗ ∙ ∗ → Integrator Gain ∙ → Deadtime [time] Step 3: Fit a Process Model Simple First Order Models for Modeling All models are wrong, some are useful -George Box
  17. 17. Copyright © 2014 Control Station, Inc. All Rights Reserved 63%∆ ∆ ∆ Process Gain  How Far How Far does the PV Move for Change in the Output Process Time Constant  How Fast How Fast does it take the PV to reach 63% of its total change Process Deadtime  How Much Delay How much delay is there from when the CO is changed until the PV first moves Step 3: Fit a Process Model First Order Plus Deadtime (Self-Regulating Model)
  18. 18. Copyright © 2014 Control Station, Inc. All Rights Reserved Integrating Process Gain  How Far and How Fast How Far and How Fast does the PV Move when the CO is moved from its balancing point Process Deadtime  How Much Delay How much delay is there from when the CO is changed until the PV first moves Step 3: Fit a Process Model First Order Plus Deadtime (Non Self-Regulating Model)
  19. 19. Copyright © 2014 Control Station, Inc. All Rights Reserved Step 3: Fit a Process Model By Hand or Autotune Approach Sufficient for Simplest of Controllers Software Modeling Much More Robust Handle Open/Close Loop Noisy / Non-Steady State Conditions Tunings Only As Good as the Model SIMPLE
  20. 20. Copyright © 2014 Control Station, Inc. All Rights Reserved Step 4: Tune the PID Loop 1 First compute, , the closed loop time constant (a small provides an aggressive or quick response) Choose your performance using these rules: aggressive: is the larger of 0.1 or 0.8 moderate: is the larger of 1 or 8 conservative: is the larger of 10 or 80 PI tuning correlations use this and the FOPDT model values: and IMC Tuning Correlation: Dependent PI, Self-Regulating Process
  21. 21. Copyright © 2014 Control Station, Inc. All Rights Reserved Step 4: Tune the PID Loop 1 First compute, , the closed loop time constant (a small provides an aggressive or quick response) Choose your performance using these rules: aggressive: is the larger of 0.1 or 0.8 moderate: is the larger of 1 or 8 conservative: is the larger of 10 or 80 PID tuning correlations use this and the FOPDT model values: 1 0.5 0.5 0.5 2 IMC Tuning Correlation: Dependent PID, Self-Regulating Process
  22. 22. Copyright © 2014 Control Station, Inc. All Rights Reserved Step 4: Tune the PID Loop 1 The closed loop time constant, , should be as large as possible, but still fast enough to arrest or recover from a major disturbance. PI tuning correlations use this and the FOPDT Integrating model values: 1 ∗ 2 2 IMC Tuning Correlation: Dependent PID, Non Self-Regulating Process
  23. 23. Copyright © 2014 Control Station, Inc. All Rights Reserved Step 4: Tune the PID Loop Flow Loops  3 to 5 times the Open Loop Time Constant, Pressure Loops  2 to 4 times the Open Loop Time Constant, Temperature Loops  1 to 3 times the Open Loop Time Constant, Closed Loop Time Constant Rules of Thumb
  24. 24. Copyright © 2014 Control Station, Inc. All Rights Reserved Step 4: Tune the PID Loop Set point tracking (servo) response as changes Expected PI Controller Response Copyright © 2007 by Control Station, Inc. All Rights Reserved. Conservative Moderate Aggressive
  25. 25. Copyright © 2014 Control Station, Inc. All Rights Reserved Kc*2 Kc/2 Kc Step 4: Tune the PID Loop Challenges of PI Control: Self-Regulating Processes Base Case Performance 2 Copyright © 2007 by Control Station, Inc. All Rights Reserved. Ti/2 Ti 2Ti
  26. 26. Copyright © 2014 Control Station, Inc. All Rights Reserved 2*Kc Kc / 2 Kc Step 4: Tune the PID Loop Challenges of PI Control: Non Self-Regulating Processes Ti/2 Ti 2Ti
  27. 27. Copyright © 2014 Control Station, Inc. All Rights Reserved Step 4: Tune the PID Loop PID shows decreased oscillations compared to PI performance PID has somewhat: Shorter Rise Time Faster Settling Time Smaller Overshoot PI vs PID Set Point Tracking Response
  28. 28. Copyright © 2014 Control Station, Inc. All Rights Reserved Step 5: Implement and Test Results Testing PID Controllers Typically Involve Adjust Set-Point to ensure adequate tracking Did the Process Variable Overshoot? Did the Controller Output Move too much? Introduce a Load Change or Disturbance Did the Process Variable Recover quick enough? Updated Tuning Parameters MUST be tested NOTE: PID Controllers work off of controller error (SP-PV), if there is no error, there is nothing for the PID Controller to do. You MUST introduce controller error, and force the controller to respond before you know if your tuning changes improved the system.
  29. 29. Copyright © 2014 Control Station, Inc. All Rights Reserved Step 6: Document, Document, Document. Who Who is accountable for the changes? What Which loop has been tuned, what were the As Found and Recommended Tuning Values? When When was the Loop Adjusted? Why Why was this particular loop tuned?
  30. 30. Copyright © 2014 Control Station, Inc. All Rights Reserved Processes Have Time Varying Behavior The CO to PV behavior described by an ideal FOPDT model is constant, but real processes change every day because: surfaces foul or corrode mechanical elements like seals or bearings wear feedstock quality varies and catalyst activity decays environmental conditions like heat and humidity change So the values of , and that best describe the dynamic behavior of a process today may not be best tomorrow As a result, controller performance can degrade with time and periodic retuning may be required
  31. 31. Copyright © 2014 Control Station, Inc. All Rights Reserved Example Process: Heat Exchanger Process Variable (PV) Set Point (SP) Controller Output (CO) Disturbances (D)
  32. 32. Copyright © 2014 Control Station, Inc. All Rights Reserved Heat Exchanger Shows Nonlinear Behavior Processes often exhibit changing (or nonlinear) behavior as operating level changes As a result, “best” tuning can change if the set point moves the PV across a range of operation
  33. 33. Copyright © 2014 Control Station, Inc. All Rights Reserved Controller’s Robust Stability What does it mean for a controller to be Robustly Stable? Controller Robustness measures the Ability to Tolerate Variations in Process Behavior (e.g., Nonlinearity) Visual Robust Stability Plot Plots Plant-Model Mismatch in Gain vs. Plant-Model Mismatch in Dead Time Stable and Unstable Regions shown on Plot
  34. 34. Copyright © 2014 Control Station, Inc. All Rights Reserved Options For Tuning Manual Tuning • Time Consuming, and may not yield consistent results. • Results vary on experience Push Button Auto-Tune • For Simple / Fast Loops (e.g. Flow) • Requires “Steady-State” Starting Condition • Generally not recommended for Level Loops or Slow Temperature (Batch Temperature) Controllers Dedicated PID Tuning / Modeling Package • Handle All Types of Processes • From Fast Flows, to Slow Batch Temperature or Furnace Temperature Control • Customize Controller Response to Match Objective
  35. 35. Copyright © 2014 Control Station, Inc. All Rights Reserved Full Non-Steady State Modeling Open and Closed Loop Modeling Supports All Rockwell Automation PLCs (SLC to Logix) Monitor 100s to 1000s of PIDs Identify Interactions, Valve and Tuning Issues Customizable Alerts and Reports Rockwell Automation Encompass Products For Controller Tuning and Control Loop Performance Monitoring
  36. 36. Copyright © 2014 Control Station, Inc. All Rights Reserved Summary First Order Models provide Important Information How Far?; How Fast?; With How Much Delay? Fit by Hand or Use Software Systematic Approach to Tune PID Controllers Internal Model Control (IMC) Tuning Uses the FOPDT Model in the Tuning Correlation Specifying the Single Adjustable Tuning Parameter, Decrease for a Faster, More Aggressive Response Increase to Increase Robustness Understanding Robust Stability Processes Change over time and with Operating Level Controller Performance can degrade over time Select Tunings which balance performance with robust stability
  37. 37. Copyright © 2014 Control Station, Inc. All Rights Reserved Questions? Thank you for attending! Contact Information: Bob Rice, PhD Vice President, Engineering +1-860-872-2920, ext. 1601 +1-860-420-7158 (m) bob.rice@controlstation.com
  38. 38. Copyright © 2014 Rockwell Automation, Inc. All Rights Reserved. We care what you think!  On the mobile app: 1. Locate session using Schedule or Agenda Builder 2. Click on the thumbs up icon on the lower right corner of the session detail 3. Complete survey 4. Click the Submit Form button 38 Please take a couple minutes to complete a quick session survey to tell us how we’re doing. 2 3 4 1 Thank you!!
  39. 39. Copyright © 2014 Rockwell Automation, Inc. All Rights Reserved. PUBLIC INFORMATION Questions?

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