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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
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