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How to Improve your PID Controller

Javier Gutierrez

LabVIEW Product Marketing

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Benefits of Advanced Control and Tuning

Model-based

control < 1%

Manual

control

PID is finePID needs

manual tuning

*Sources: Cybosoft and ExperTune

• A poorly tuned control valve costs additional $880/year*

• A bad pH loop incurred chemical waste of $50,000/month*

• A bad kiln temp loop cost $30,000/month*

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Agenda

• What is PID?

• How to improve performance

Hardware considerations

Upgrade PID Algorithm

Advanced Controllers

• Conclusion

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Agenda

• What is PID?

• How to improve performance

Hardware considerations

Upgrade PID Algorithm

Advanced Controllers

• Conclusion

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• Set Point (SP) – Desired control point

• Output (OP) – Controller output

• Process Variable (PV) – Plant/process output

• Error = SP - PV

SP

OP

PV

error

What is PID

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PID Parameters

• Proportional Drive to setpoint

Error → 0, OP → 0

“Steady-state error”

• Integral Eliminate steady state error

OP proportional to ∫ error

• Derivative Increase response rate

OP proportional to rate of change of error

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System to control

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PID Implementation Demo

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PID Control – Pros and Cons

• Advantages

Proven

Easy to implement

• Disadvantages

Not easy to tune

Not suitable for all systems

• Backlash, friction, and so on

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Agenda

• What is PID?

• How to improve performance

Hardware considerations

Upgrade PID Algorithm

Advanced Controllers

• Conclusion

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How to program PID

Windows/Real Time

FPGA

Function Blocks

Control and Simulation

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Benefits of Higher Loop Rates

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PID Loop rates

600 Hz

25 kHz

100 kHz

1 MHz

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Die Casting Machine

Copyright 2007 © EUROelectronics srl – ITALY -

The movement of the aluminium injection plunger controlled in a steady

closed loop at a speed varying from 0 up to 10 m/sec.

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Agenda

• What is PID?

• How to improve performance

Hardware considerations

Improve PID Algorithm

Advanced Controllers

• Conclusion

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Upgrade your PID

Disturbances

Non Linear

Time Variant

Feed-forward

Gain Scheduling

Adaptive PID

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Feed-Forward

• Commonly used to compensate for a

measurable external disturbance before it affects

a controlled variable.

• e.g. product feed rate changes

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Gain Scheduling

• Used to change gain on real-time depending on

OV.

• Bumpless transfers

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Adaptive PID

• Mixed of On-Line system identification and

common PID control.

• Can handle time-variant systems

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Agenda

• What is PID?

• How to improve performance

Hardware considerations

Upgrade PID Algorithm

Advanced Controllers

• Conclusion

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Advanced Controllers

National Instruments

• Optimal Controllers (LQR, LQG)

• Model Predictive Control (MPC)

• Kalman Filters

• Fuzzy Logic

Third Party Partners

• Neural Networks

• Genetic Algorithms

• Model Free Adaptive

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How to create an advanced Controller

• Datalogging

• System Identification

• Model Validation

Plant Modeling

• Design

• Simulation

Control Design• Deployment

• Test

Implementation

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Temp Chamber - Experiment

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Plant Modeling - Validation

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MPC Control Design

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MPC Control Prototype

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Advanced Controllers

• Pros/Cons

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Agenda

• What is PID?

• How to improve performance

Hardware considerations

Upgrade PID Algorithm

Advanced Controllers

• Conclusion

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Conclusions

• PID

• Consider

Upgrading hardware

Enhance PID Algorithm

Upgrading Control Algorithm