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

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

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

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

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Control and applications, IASTED, Canada Time Domain Specification

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Control and applications, IASTED, Canada General Tuning Guidelines

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

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

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Control and applications, IASTED, Canada Inference Rules

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Control and applications, IASTED, Canada Defuzzification

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

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Control and applications, IASTED, Canada Evaporator example

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

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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

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