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E-raamat: Optimization of PID Controllers Using Ant Colony and Genetic Algorithms

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Optimization of PID Controllers Using Ant Colony and Genetic AlgorithmsSuurem pilt
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Artificial neural networks, genetic algorithms and the ant colony optimization algorithm have become a highly effective tool for solving hard optimization problems. As their popularity has increased, applications of these algorithms have grown in more than equal measure. While many of the books available on these subjects only provide a cursory discussion of theory, the present book gives special emphasis to the theoretical background that is behind these algorithms and their applications. Moreover, this book introduces a novel real time control algorithm, that uses genetic algorithm and ant colony optimization algorithms for optimizing PID controller parameters. In general, the present book represents a solid survey on artificial neural networks, genetic algorithms and the ant colony optimization algorithm and introduces novel practical elements related to the application of these methods to process system control.

This book covers the theory behind artificial neural networks, genetic algorithms and the ant colony optimization algorithm, and presents a novel real time control algorithm using genetic and ant colony optimization algorithms for optimizing PID controllers.
List of Symbols
ix
List of Abbreviations
xiii
List of Figures
xv
List of Tables
xix
Introduction 1(4)
1 Artificial Neural Networks
5(14)
1.1 Artificial Neural Network Cell (Perceptron)
5(2)
1.2 Artificial Neural Network Models
7(8)
1.2.1 Feedforward Network Models
7(2)
1.2.2 Recurrent Networks
9(1)
1.2.3 Learning Rules
9(2)
1.2.4 Multilayer Perceptron
11(1)
1.2.5 Error Backpropagation Algorithm
12(1)
1.2.6 The Nonlinear Autoregressive Network with Exogenous Inputs (NARX) Type ANN
13(2)
1.3 Modeling of a System with ANN
15(4)
1.3.1 Advanced Modeling (Series-Parallel Modeling)
15(4)
2 Genetic Algorithm
19(12)
2.1 Types of Genetic Algorithms
21(1)
2.1.1 Micro Genetic Algorithm (μGA)
21(1)
2.1.2 Steady-State Genetic Algorithm
22(1)
2.1.3 Hierarchic Genetic Algorithm (HGA)
22(1)
2.1.4 Messy Genetic Algorithm (mGA)
22(1)
2.2 Scheme Theorem and Genetic Algorithm Operators
22(9)
2.2.1 Coding (G)
23(1)
2.2.2 Creating First Population (Σ)
23(1)
2.2.3 Size of the Population (μ)
23(1)
2.2.4 Reproduction (Gap)
24(1)
2.2.5 Selection (S)
24(2)
2.2.6 Crossover (ωc)
26(1)
2.2.7 Mutation (ωm)
27(1)
2.2.8 Fitness Value Scaling
28(3)
3 Ant Colony Optimization (ACO)
31(6)
3.1 Real Ants
31(2)
3.2 Artificial Ants
33(1)
3.3 Ant Colony Algorithm
34(3)
3.3.1 Pheromone Vaporization
35(2)
4 An Application for Process System Control
37(32)
4.1 Modeling of the System with ANN
39(5)
4.1.1 Collection of the Training Data
40(2)
4.1.2 Data Normalization
42(1)
4.1.3 NN Training
42(2)
4.2 PID Controller Design with GA
44(10)
4.2.1 Structure of the Designed Controller
44(2)
4.2.2 Genetically Coding of the PID Parameters
46(1)
4.2.3 Learning of the PID Parameters by GA
46(1)
4.2.4 Transformation of the System Output to GA Conformity Value
47(1)
4.2.5 Used Genetic Operators
47(1)
4.2.6 Performance of the Genetic-PID Controller
48(6)
4.3 PID Controller Design with ACO
54(8)
4.3.1 ACO Parameters
56(2)
4.3.2 Performance of the ACO-PID Controller
58(4)
4.4 Ziegler-Nichols (ZN) Method on PID Computation
62(7)
4.4.1 System Response to Step Inputs
64(1)
4.4.2 System Behavior to the Transition between Step Inputs
65(1)
4.4.3 Behavior of the System When Distortion Was Imported to the System
66(1)
4.4.4 Trajectory Tracking Responses of the ZN-PID Controller
66(3)
5 Conclusion
69(4)
Appendix
73(6)
A.1 Matlab Codes for ACO
73(6)
A.1.1 Ant Colony Optimization Solver
73(4)
A.1.2 ACO Cost Function
77(1)
A.1.3 Run ACO
78(1)
References 79(4)
Index 83
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