1. www.natinst.com PID and Fuzzy Logic Control Systems John Limroth, Software Engineer Yiannis Pavlou, Applications Engineer Tues, 10:15a and 11:30a Wed. 10:15a, 11:30a, 12:45p, 2:00p, 3:30p, and 4:45p Exhibit Hall (3B) John Limroth, Software Engineer Yiannis Pavlou, Applications Engineer Tues, 10:15a and 11:30a Wed. 10:15a, 11:30a, 12:45p, 2:00p, 3:30p, and 4:45p Exhibit Hall (3B)

2. www.natinst.com Overview  Control technology PID control Fuzzy logic  National Instruments products for control LabVIEW RT PID Control Toolset for LabVIEW  Control technology PID control Fuzzy logic  National Instruments products for control LabVIEW RT PID Control Toolset for LabVIEW

3. www.natinst.com Control Terminology Terms:  Process variable  Setpoint  Controller output  Plant Terms:  Process variable  Setpoint  Controller output  Plant Examples:  Temperature  Desired temperature  Heater voltage  Furnace

4. www.natinst.com PID Control

5. www.natinst.com PID Parameters  Proportional gain – K c  Integral gain – K c /T i T i is the integral time constant or “reset time”  Derivative gain – K c *T d T d is the derivative time constant or “rate time”  Proportional gain – K c  Integral gain – K c /T i T i is the integral time constant or “reset time”  Derivative gain – K c *T d T d is the derivative time constant or “rate time”

6. www.natinst.com PID Gains – P  Proportional gain K c – “The Sledgehammer” Provides immediate controller response to setpoint change, but PV may not settle exactly on SP using proportional control alone  Proportional gain K c – “The Sledgehammer” Provides immediate controller response to setpoint change, but PV may not settle exactly on SP using proportional control alone

7. www.natinst.com PID Parameter Tuning – P only

8. www.natinst.com PID Gains – PI  Proportional gain K c – “The Sledgehammer” Provides immediate controller response to setpoint change, but PV may not settle exactly on SP using proportional control alone  Integral gain K c / T i – Fine tuning Integrates the error over time to overcome the offset from Proportional alone such that PV = SP. However, integral action may cause overshoot, oscillation, and/or instability problems  Proportional gain K c – “The Sledgehammer” Provides immediate controller response to setpoint change, but PV may not settle exactly on SP using proportional control alone  Integral gain K c / T i – Fine tuning Integrates the error over time to overcome the offset from Proportional alone such that PV = SP. However, integral action may cause overshoot, oscillation, and/or instability problems

9. www.natinst.com PID Parameter Tuning – PI only

10. www.natinst.com PID Gains – PID  Proportional gain K c – “The Sledgehammer” Provides immediate controller response to setpoint change, but PV may not settle exactly on SP using proportional control alone  Integral gain K c / T i – Fine tuning Integrates the error over time to overcome the offset from Proportional alone such that PV = SP. However, Integral action may cause overshoot, oscillation and/or instability problems  Derivative gain K c * T d – Whoa… Used to put the reigns on PI control to prevent overshoot and oscillation and to add stability  Proportional gain K c – “The Sledgehammer” Provides immediate controller response to setpoint change, but PV may not settle exactly on SP using proportional control alone  Integral gain K c / T i – Fine tuning Integrates the error over time to overcome the offset from Proportional alone such that PV = SP. However, Integral action may cause overshoot, oscillation and/or instability problems  Derivative gain K c * T d – Whoa… Used to put the reigns on PI control to prevent overshoot and oscillation and to add stability

11. www.natinst.com PID Parameter Tuning – PID

12. www.natinst.com PID Autotuning

13. www.natinst.com Fuzzy Logic Dataflow

14. www.natinst.com Fuzzy Logic Control for LabVIEW Why is fuzzy logic important?  Easy to implement an intuitive control strategy  Better control of non-linear systems PID control is linear Fuzzy control is non-linear Why is fuzzy logic important?  Easy to implement an intuitive control strategy  Better control of non-linear systems PID control is linear Fuzzy control is non-linear

15. www.natinst.com Rule-Based Control  Example “If temperature is high, then heater voltage output should be low.”  Membership sets What is meant by “high?”  Example “If temperature is high, then heater voltage output should be low.”  Membership sets What is meant by “high?”

16. www.natinst.com Fuzzy Sets  Boolean (or two-valued) sets: Members belong to a set – non-members do not Traditional Boolean values (on/off, 1/0)  Fuzzy Sets: Partial membership to set allowed Values along continuum of 0 to 1  Boolean (or two-valued) sets: Members belong to a set – non-members do not Traditional Boolean values (on/off, 1/0)  Fuzzy Sets: Partial membership to set allowed Values along continuum of 0 to 1

17. www.natinst.com Boolean Set – “High Body Temperature” Temperature Membership

18. www.natinst.com Fuzzy Set – “High Body Temperature” Temperature Membership

19. www.natinst.com Fuzzy Logic Control

20. www.natinst.com Fuzzy Logic and PID Combined

21. www.natinst.com Fuzzy Logic Design Software

22. www.natinst.com Overview  Control technology PID control Fuzzy logic  National Instruments products for control LabVIEW RT PID Control Toolset for LabVIEW  Control technology PID control Fuzzy logic  National Instruments products for control LabVIEW RT PID Control Toolset for LabVIEW