Presentation given during NIWeek 2008 about basics on PID and tips on how to improve it

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2. How to Improve your PID Controller
Javier Gutierrez
LabVIEW Product Marketing
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3. Benefits of Advanced Control and Tuning
Model-based Manual
• A poorly tuned control control < 1% 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* PID needs
PID is fine
manual tuning
*Sources: Cybosoft and ExperTune
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4. Agenda
• What is PID?
• How to improve performance
 Hardware considerations
 Upgrade PID Algorithm
 Advanced Controllers
• Conclusion
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5. Agenda
• What is PID?
• How to improve performance
 Hardware considerations
 Upgrade PID Algorithm
 Advanced Controllers
• Conclusion
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6. What is PID
• Set Point (SP) – Desired control point
• Output (OP) – Controller output
• Process Variable (PV) – Plant/process output
• Error = SP - PV
error OP
SP PV
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7. 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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8. System to control
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9. PID Implementation Demo
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10. 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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11. Agenda
• What is PID?
• How to improve performance
 Hardware considerations
 Upgrade PID Algorithm
 Advanced Controllers
• Conclusion
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12. How to program PID
Function Blocks
Windows/Real Time
FPGA Control and Simulation
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13. Benefits of Higher Loop Rates
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14. PID Loop rates
1 MHz
100 kHz
25 kHz
600 Hz
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15. Die Casting Machine
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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Copyright 2007 © EUROelectronics srl – ITALY -

16. Agenda
• What is PID?
• How to improve performance
 Hardware considerations
 Improve PID Algorithm
 Advanced Controllers
• Conclusion
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17. Upgrade your PID
Disturbances Feed-forward
Non Linear Gain Scheduling
Time Variant Adaptive PID
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18. 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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19. Gain Scheduling
• Used to change gain on real-time depending on
OV.
• Bumpless transfers
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20. Adaptive PID
• Mixed of On-Line system identification and
common PID control.
• Can handle time-variant systems
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21. Agenda
• What is PID?
• How to improve performance
 Hardware considerations
 Upgrade PID Algorithm
 Advanced Controllers
• Conclusion
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22. 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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23. How to create an advanced Controller
Control Design
• Datalogging • Deployment
• System • Design • Test
Identification • Simulation
• Model Validation
Plant Modeling Implementation
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24. Temp Chamber - Experiment
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25. Plant Modeling - Validation
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26. MPC Control Design
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27. MPC Control Prototype
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28. Advanced Controllers
• Pros/Cons
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29. Agenda
• What is PID?
• How to improve performance
 Hardware considerations
 Upgrade PID Algorithm
 Advanced Controllers
• Conclusion
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30. Conclusions
• PID
• Consider
 Upgrading hardware
 Enhance PID Algorithm
 Upgrading Control Algorithm
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