control station, inc.: pid controller tuning: advancing the state-of-the-art with patent-pending...

Copyright © 2014 Rockwell Automation, Inc. All Rights Reserved.

PUBLIC INFORMATION

PID Controller TuningAdvancing the State-of-the-Art with Patent-Pending Modeling Control Station

Robert Rice, PhDVice President, Engineering

Control Station, Inc.

PID Controller Tuning Advancing the State-of-the-Art with Patent-Pending Modeling

Copyright © 2014 Control Station, Inc. All Rights Reserved

Outline of Discussion

Introduction to Process ControlBrief history of Process Control

Introduction to Process Behavior and the Control Objective

Why understanding the process is fundamental to controlling itThe importance of stating the correct control objective

PID Controller Tuning MethodThe PID Controller

What is a PID Controller Examples of the PID controllers (e.g. PI vs PID)

Theory Vs the Real-WorldQuestions and Answers


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 RevolutionCentrifugal (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 ManufacturingPneumatic, Electronic, Model Predictive ControllersPID Control


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.


Process OptimizationUnderperforming 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%


Significant Opportunity:Uncovering the Value of Improved Control

ProductionThroughput

Production Yield

EnergyConsumption

ProductionDefects

2 – 5%

5 – 15% 25 – 50%

5 – 10%

Benefits of regular PID tuning can be found across a production facility:


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


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 IssuesProvide Root-Cause DetectionRecommendation for Corrective ActionDisplay Customizable Reports


Step 1: Find Controller, Specify ObjectiveGood Control is “SIMPLE”


Step 1: Find Controller, Specify Objective

What is/are the primary Control Objective(s)?Maintain Liquid Level In the Reflux DrumMaintain Column StabilityPrevent Environmental Release by avoid Drum Hi Limit

Reflux Drum – Level Control Example


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 ProcessData Should Show “Cause and Effect”


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 ProcessGood Bumps Tests

sharp COmovement

sharp COmovement


Step 2: Step or Bump the ProcessBad Bump Tests

AVIODDisturbance Driven Data &

Slow Ramp CO Changes


Step 3: Fit a Process Model

Self-RegulatingIf all inputs & outputs are held constant, the process will seek a steady-stateEx: Heat Exchanger

Non Self-RegulatingProcess will only reach a steady-state at its ‘balancing’ pointEx: Surge Tank

Types of Process Behavior


Self-Regulating

∙

→ Process Gain

→ Time Constant [time]→ Deadtime [time]

Non Self-Regulating

∗ ∙

∗ → Integrator Gain ∙

→ Deadtime [time]

Step 3: Fit a Process ModelSimple First Order Models for Modeling

All models are wrong, some are useful -George Box


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 ModelFirst Order Plus Deadtime (Self-Regulating Model)


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 ModelFirst Order Plus Deadtime (Non Self-Regulating Model)


Step 3: Fit a Process Model

By Hand or Autotune Approach Sufficient for Simplest of ControllersSoftware Modeling Much More Robust

Handle Open/Close LoopNoisy / Non-Steady State Conditions

Tunings Only As Good as the Model

SIMPLE


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 0.8moderate: is the larger of 1 or 8conservative: 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



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 0.8moderate: is the larger of 1 or 8conservative: is the larger of 10 or 80

PID tuning correlations use this and the FOPDT model values:

1 0.50.5 0.5 2

IMC Tuning Correlation: Dependent PID, Self-Regulating Process



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∗

22

IMC Tuning Correlation: Dependent PID, Non Self-Regulating Process



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



Set point tracking (servo) response as changes

Expected PI Controller Response

Copyright © 2007 by Control Station, Inc. All Rights Reserved.

Conservative Moderate Aggressive


Kc*2

Kc/2

Kc

Step 4: Tune the PID LoopChallenges of PI Control: Self-Regulating Processes

Base Case Performance

2

Copyright © 2007 by Control Station, Inc. All Rights Reserved.

Ti/2 Ti 2Ti


2*Kc

Kc / 2

Kc

Step 4: Tune the PID LoopChallenges of PI Control: Non Self-Regulating Processes

Ti/2 Ti 2Ti



PID shows decreased oscillations compared to PI performancePID has somewhat:

Shorter Rise TimeFaster Settling TimeSmaller Overshoot

PI vs PID Set Point Tracking Response


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.


Step 6: Document, Document, Document.

WhoWho is accountable for the changes?

WhatWhich loop has been tuned, what were the As Found and Recommended Tuning Values?

WhenWhen was the Loop Adjusted?

WhyWhy was this particular loop tuned?


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 wearfeedstock 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


Example Process: Heat Exchanger

Process Variable (PV)Set Point (SP)Controller Output (CO)Disturbances (D)


Heat Exchanger Shows Nonlinear Behavior

Processes often exhibit changing (or nonlinear) behavior as operating level changesAs a result, “best” tuning can change if the set point moves the PV across a range of operation


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 PlotPlots Plant-Model Mismatch in Gain vs. Plant-Model Mismatch in Dead TimeStable and Unstable Regions shown on Plot


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


Full Non-Steady State ModelingOpen and Closed Loop ModelingSupports All Rockwell Automation PLCs (SLC to Logix)

Monitor 100s to 1000s of PIDsIdentify Interactions, Valve and Tuning IssuesCustomizable Alerts and Reports

Rockwell Automation Encompass ProductsFor Controller Tuning and Control Loop Performance Monitoring


Summary

First Order Models provide Important InformationHow 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 CorrelationSpecifying the Single Adjustable Tuning Parameter,

Decrease for a Faster, More Aggressive ResponseIncrease to Increase Robustness

Understanding Robust StabilityProcesses Change over time and with Operating LevelController Performance can degrade over timeSelect Tunings which balance performance with robust stability


Questions?

Thank you for attending!

Contact Information:

Bob Rice, PhD

Vice President, Engineering+1-860-872-2920, ext. 1601+1-860-420-7158 (m)[email protected]


We care what you think!

On the mobile app:1. Locate session using

Schedule or Agenda Builder2. Click on the thumbs up icon on

the lower right corner of the session detail

3. Complete survey4. 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!!


PUBLIC INFORMATION

Questions?

control station, inc.: pid controller tuning: advancing the state-of-the-art with patent-pending...

Technology

modeling control station

engineering control

objective good control

rights reserved pid

controlled process

issues control systems

process data model

tuning process document