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Fuzzy Adaptive Internal Model Control

Fuzzy Adaptive Internal Model Control. 學生 : 朱福氣 M96720062. The control scheme consists of two parts:. a fuzzy dynamic model : serves as the internal model of the FAIMC is identified online by using the input and output measurement of the plant.

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Fuzzy Adaptive Internal Model Control

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  1. Fuzzy Adaptive Internal Model Control 學生:朱福氣 M96720062

  2. The control scheme consists of two parts: • a fuzzy dynamic model:serves as the internal model of the FAIMC is identified online by using the input and output measurement of the plant. • a model-based fuzzy controller : Based on the identified fuzzy model, the fuzzy controller is designed to minimize and H2 performance objective.

  3. I.INTRODUCTION

  4. The IMC design is lucid for the following two reasons: • 1) it separates the tracking problem from the regulation problem. • 2) the design of the controller is relatively straightforward.

  5. the major task for the design of an IMC controller is to find a precise model of the plant. • these methods fall into three categories: 1) conventional modeling 2) fuzzy modeling 3) neural network modeling

  6. Section II describes the fuzzy dynamic model and adaptation law of fuzzy identification. • Section III contains the design of a model- based fuzzy controller and the implementation of the FAIMC algorithm. • In Section IV, the stability analysis of the FAIMC system is given. • In Section V, experimental results and a comparison with a conventional IMC (CIMC) system are given. • Section VI concludes the paper by summarizing the main results.

  7. II. FUZZY DYNAMIC MODEL

  8. A. Fuzzy Rule Base • I=1~nk, the number of the fuzzy rules • is the output of the th local linear system • u(t) is the input of the plant • is the rational transfer function of the local linear system.

  9. The fuzzy rule can be expressed in time-response form :

  10. B. Update Law of Fuzzy Identification

  11. III. MODEL-BASED FUZZY CONTROLLER • For each local linear system, the relevant local feedforward controller is selected to yield a “good” system response for the input of interest. That means the local controller is integral-square-error (ISE) or H2-optimal

  12. A. Local Controller Design

  13. B. Fuzzy Controller Design

  14. C. Implementation of FAIMC

  15. IV. STABILITY ANALYSIS OF FAIMC

  16. 因為

  17. V. EXPERIMENTAL RESULTS

  18. VI. CONCLUSION • In this paper, a FAIMC has been developed. This controlstructure consists of fuzzy dynamic model and model-basedfuzzy controller. The distinguished feature of fuzzy dynamicmodeling is that it aims at identifying the fuzzy continuous-timemodel of the plant by using the input and output measurementsof the plant.

  19. The model-based fuzzy controller is designed basedon the identified fuzzy dynamic model. The design principle isthat all fired fuzzy local linear systems are driven to arrive atthe H2-optimal control objective.

  20. An algorithm for the real-timeimplementation of the FAIMC system has been developed and the stability analysis about the FAIMC system has been provided.The control system has been successfully applied to flowrate control in the PCU. • The experimental results demonstratethat the control system has very good robust performance.

  21. Thank you

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