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Copyright © 2008 Rockwell Automation, Inc. All rights reserved.

Advanced Process control instructions with Logix

Copyright © 2008 Rockwell Automation, Inc. All rights reserved. 2

What is APC?

• APC = Advanced Process Control• A traditional control system controls processes to fixed set points

determined by operators. Uses generic control algorithms and instructions such as a PID controller.

• The purpose of an APC system is to automatically account for an expected (modeled, predicted…) process response and calculate “optimal” control actions to minimize process variation.

• APC systems utilize controllers and technologies such as – Model based control – Fuzzy logic and control – Multivariable control – Adaptive control – Inferential control – Process modeling and simulation


Benefits of APC?

Economic benefits

Throughput increase 4 - 10%Yield increase 0.1 – 10%Energy savings 3 - 10%Variation reduction 20 – 90%

Reduce variation of key process and quality parametersIncrease plant capacity via tighter and smarter controlsReact correctly and quickly to changing conditionsTypical payback period is <2 years, typically 6-12 months

SPECIFICATION OR LIMIT

TIMEK

EY T

AR

GET


Example Hierarchy of Control Strategies

• Manual, On/Off, Open loop• Ratio• Feedback – PID• Cascade• Feed forward (multivariable)• Controller-based APC (Regulatory)

– APC: Adaptive – Non-linear – Fuzzy– APC: Model based (multivariable)

• MPC: Model Predictive Control – Minimization of variation, energy…– Maximization of yield, throughput…

Manual

Automatic

Calculate setpoints!


RSLogix5000 FuzzyDesigner

FuzzyDesigner RSLogix 5000

• Development cycle– Design fuzzy system (FD)– Generate AOI (FD)– Import and instantiate AOI (RS5K)– Download project to Logix (RS5K)– Monitor and tune (FD, RS5K)

monitoring tuning

OPC

RSLinx Classic

Add-On Instruction(.L5X)


Direct Fuzzy Controller

• Typically handles multiple inputs and generates multiple outputs• Recommended for experienced designers as control variables are direct functions of

rules• Number of rules increases rapidly with number of inputs and fuzzy terms for inputs• Dimensionality can, however, be reduced by hierarchical structuring of the rule base of

the controller, which is supported by FuzzyDesigner.

PLANTPLANTFUZZY CONTROLLER

ControlS etpoints

Input filter

Process Variables

PLANTPROCESSFUZZY CONTROLLER

ControlVariables

Setpoints

Output filter

Control system status Primary controls

Process Variables


Fuzzy PID Supervisor: Typical Application

• Applied to existing or newly designed control• Easy to design non-linear control• Uses expert knowledge and rules to manipulate controller parameters in real time

plant state information

SP

PV

FUZZY SUPERVISOR

FUZZY SUPERVISOR

PLANTPLANT

PIDgains

CVPID or MPC

CONTROLLERPID or MPC

CONTROLLER

feedforward


MPC Function Blocks – Logix v17

• V17 of RSLogix 5000 is adding three new instructions for APC applications (IMC, CC, and MMC).– Useful for applications with interacting inputs/outputs.– Useful for applications with long deadtimes.


Internal Model Control (IMC)

• Controls a single process variable by manipulating a single output.

• Compares actual process error against error calculated by an internal first order lag plus deadtime model.

• Built-in autotuner makes setup easier.• Suitable for long deadtime processes which are

difficult to control with standard PID loops.• Setup and configuration parameters very similar to

PIDE

IMC_01

IMC ...

Internal Model Control0.0

PV0.0

SPProg0.0

SPCascade0.0

RatioProg0.0

CVProg0.0

HandFB0

ProgProgReq0

ProgOperReq0

ProgCasRatReq0

ProgAutoReq0

ProgManualReq0

ProgOverrideReq0

ProgHandReq

CVEU0.0

SP0.0

ProgOper0

CasRat0

Auto0

Manual0

Override0

Hand0


Coordinated Control (CC)

• Controls a single process variable by manipulating as many as three different outputs.

• Target values and priorities for outputs are used to optimize your process.

• Compares actual process error against error calculated by internal first order lag plus deadtime models for each output.

• Outputs not currently controlling (held at target or in manual) may be used as feedforward signals.

• Built-in autotuners make setup easier.

CC_01

CC ...

Coordinated Control0.0

PV0.0

SPProg0.0

CV1Prog0.0

CV2Prog0

CV3Prog0

ProgProgReq0

ProgOperReq0

ProgCV1AutoReq0

ProgCV2AutoReq0

ProgCV3AutoReq0

ProgCV1ManualReq0

ProgCV2ManualReq0

ProgCV3ManualReq0

ProgCV1OverrideReq0

ProgCV2OverrideReq0

ProgCV3OverrideReq

CV1EU0.0

CV2EU0.0

CV3EU0.0

SP0.0

ProgOper0

CV1Auto0

CV2Auto0

CV3Auto0

CV1Manual0

CV2Manual0

CV3Manual0

CV1Override0

CV2Override0

CV3Override0


Modular Multivariable Control (MMC)

• Controls two process variables to their setpoints using up to three controller outputs.

• Target values and priorities for outputs are used to optimize your process.

• Compares actual process errors against errors calculated by internal first order lag plus deadtime models for each output-to-input relationship.

• Output not currently controlling (held at target or in manual) may be used as a feedforward signal.

• Built-in autotuners make setup easier.

MMC_01

MMC ...

Modular Multivariable Control0.0

PV10.0

PV20.0

SP1Prog0.0

SP2Prog0.0

CV1Prog0.0

CV2Prog0

CV3Prog0

ProgProgReq0

ProgOperReq0

ProgCV1AutoReq0

ProgCV2AutoReq0

ProgCV3AutoReq0

ProgCV1ManualReq0

ProgCV2ManualReq0

ProgCV3ManualReq0

ProgCV1OverrideReq0

ProgCV2OverrideReq0

ProgCV3OverrideReq

CV1EU0.0

CV2EU0.0

CV3EU0.0

SP10.0

SP20.0

ProgOper0

CV1Auto0

CV2Auto0

CV3Auto0

CV1Manual0

CV2Manual0

CV3Manual0

CV1Override0

CV2Override0

CV3Override0


Hands-On Lab

• Hardware– Runs in an ControlLogix controller in your station

• Software– RSlogix 5000 Version V17

• Activities– Internal Model Control (IMC) lab

• Compare PID control to IMC control– Coordinated Control (CC) lab

• Control Dissolved oxygen using three variables– Agitator speed– Air flow– Air Pressure

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