1. Tuning of Model Predictive Controllers Using Fuzzy Logic Emad Ali King Saud University Saudi Arabia

2. Control and applications, IASTED, Canada Presentation Outline Objectives MPC Control Law Time-domain Performance Tuning procedure Simulation Example Conclusion

3. Control and applications, IASTED, Canada Objectives To achieve good MPC performance To simplify the MPC tuning procedure

4. Control and applications, IASTED, Canada MPC Control Law Subject to: where:

5. Control and applications, IASTED, Canada Time Domain Specification

6. Control and applications, IASTED, Canada General Tuning Guidelines

7. Control and applications, IASTED, Canada Specification violation measure Upper bound violation: Lower bound violation: Bound violation rate:

8. Control and applications, IASTED, Canada Fuzzification of the bound violation

9. Control and applications, IASTED, Canada Inference Rules

10. Control and applications, IASTED, Canada Defuzzification

11. Control and applications, IASTED, Canada Tuning Parameter Adaptation Each sampling instant, set: = + w ( ) P = P + w (P) P

12. Control and applications, IASTED, Canada Evaporator example

13. Control and applications, IASTED, Canada A series of set point changes

14. Control and applications, IASTED, Canada Conclusions Tuning of MPC parameters is simplified using Fuzzy logic General well-known tuning guidelines are easily incorporated Improved feedback performances are obtained Computational load is kept at minimum