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Student Number 89521081
Author Meng-Ru Tsai(蔡孟儒)
Author's Email Address No Public.
Statistics This thesis had been viewed 2968 times. Download 1950 times.
Department Electrical Engineering
Year 2001
Semester 2
Degree Master
Type of Document Master's Thesis
Language English
Title The Design and Application of Control Using Dynamic Grey Prediction System
Date of Defense 2002-05-29
Page Count 53
Keyword
  • grey prediction control
  • Abstract In this thesis, we will discuss the dynamic grey prediction systems. We try to integrate the advantages of fuzzy theory, grey prediction control, and genetic algorithm to develop a dynamical prediction control system. We pay our attention to the switch of the distinct grey prediction modes. It is different from the traditional grey prediction systems that usually focus on the selection of dynamic prediction steps. Furthermore, we also analyze the opportune moments for forecasting the system behaviors. We can know that there is not all of the control processes suitable to implement the prediction control. So we should modify the framework of the controller. Finally, we use genetic algorithms to help us for designing such a complex system. From the results of simulated experiments, we can see that the proposed methods make the performance response to track the desired trajectory well.
    Table of Content The Design and Application of Control Using Dynamic Grey Prediction System
    Chapter 1 Introduction .................................. 1
    1.1 Background .......................................... 1
    1.2 Motivation........................................... 2
    1.3 Organization ........................................ 3
    Chapter 2 Grey Prediction .......................................... 4
    2.1 Introduction .......................................... 4
    2.2 The Mapped Generating and its Inverse Operator (MGO and IMGO) ........ 4
    2.3 The Complete Prediction Process .......................................... 5
    Chapter 3 Genetic Algorithm .......................................... 8
    3.1 Introduction .......................................... 8
    3.2 The Basic Operators in GA .......................................... 8
    3.2.1 Reproduction .......................................... 9
    3.2.2 Crossover .......................................... 9
    3.2.3 Mutation .......................................... 10
    3.3 Elite Method .......................................... 10
    3.4 Reinforced Search Method .......................................... 11
    Chapter 4 Application of a Dynamic Grey Prediction System Using Fuzzy Logic to
         Switch the Prediction Modes ........................... 13
    4.1 Introduction .......................................... 13
    4.2 Grey Prediction Controller .......................................... 14
    4.3 Genetic-based and Fuzzy-switching GPC ................................... 17
    4.4 Simulation and Discussion .......................................... 20
    4.5 Conclusion .......................................... 25
    Chapter 5 An Alternative Approach for the Switching Grey Prediction Controller
         .............. 26
    5.1 Introduction .......................................... 26
    5.2 Problem Formulation .......................................... 26
    5.3 The Proposed Method .......................................... 29
    5.4 Simulation .......................................... 31
    5.5 Conclusion .......................................... 33
    Chapter 6 Conclusions and Recommendations .............................. 34
    References .......................................... 36
    List of Figures
    Fig. 2.1 The prediction procedure of a simple grey predictor ............. 6
    Fig. 3.1 The complete flow chart of the genetic algorithm ................. 12
    Fig. 4.1 The framework of a simple grey prediction controller ............ 14
    Fig. 4.2 The output response of G(s) using a positive step in GPC ...... 15
    Fig. 4.3 The output response of G(s) using a negative-step in GPC ....... 15
    Fig. 4.4 The framework of the switching grey prediction PID controller ...... 16
    Fig. 4.5 The Block Diagram of the Genctic-based and Fuzzy-switching GPC
         controller .......................................... 17
    Fig. 4.6 The fuzzy sets in the premise part of the fuzzy inference scheme ....18
    Fig. 4.7 The fuzzy sets in the consequence part of the fuzzy inference
         scheme .........18
    Fig. 4.8 The step response of Gp(s) using GB-FSGPC method (9 parameters in
         searching .......................................... 20
    Fig. 4.9 The step response with kp in [5,15] ......................... 22
    Fig. 4.10 The step response with wn=1 and wp=1 ............................ 23
    Fig. 4.11 The system response with Ziegler-Nichols PID rules .............. 25
    Fig. 5.1 The results of feeding Unit step signal into the stable plant Gp(s)
         (Eq. 4.9 ) .......................................... 27
    Fig. 5.2 The results of feeding Unit step signal into the unstable plant
         Gu(s) ...................28
    Fig. 5.3 The results of feeding an Inverse Unit step input signal into the
         unstable plant .......................................... 29
    Fig. 5.4 An Alternative Approach of the SGPC controller ................ 29
    Fig. 5.5 The output response of controlling the Gp(s) by the proposed method
         .......... 32
    Fig. 5.6 The output response of controlling the unstable plant G2(s) ...... 33
    List of Table
    Table 4.1 The code length of the searched arguments in the proposed method
         (GB-FSGPC) .......................................... 20
    Table 4.2 The system parameters of Gp(s) using GB-FSGPC method .......... 21
    Table 4.3 The performance indices of the system Gp(s) using GB-FSGPC design
         .............. 21
    Table 4.4 The searched results with kp in [5,15] ......................... 21
    Table 4.5 The performance indices with kp in [5,15] ..................... 22
    Table 4.6 The searching results with wn=1 and wp=1 ..................... 22
    Table 4.7 The performance indices with wn=1 and wp=1 .................... 23
    Table 4.8 The Ziegler-Nichols PID rules ................................ 24
    Table 4.9 The system arguments with the Ziegler-Nichols rules .............. 24
    Table 4.10 The performance indices with the Ziegler-Nichols rules ........... 25
    Table 5.1 The system parameters of the design to control Gp(s) (a third-order
         stable plant) .......................................... 31
    Table 5.2 The performance indices of Gp(s) (a third-order stable plant) .... 31
    Table 5.3 The system parameters of controlling the unstable plant G2(s)
         ( a third-order unstable plant ) ............................. 32
    Table 5.4 The performance indices of controlling G2(s) ( a third-order unstable
         plant ) .................................. 32
    Reference Reference
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    Advisor
  • Yau-Tarng Juang(莊堯棠)
  • Files
  • 89521081.pdf
  • approve immediately
    Date of Submission 2002-06-03

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