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E-raamat: Robust Control Systems with Genetic Algorithms

(University of Sheffield University of Sheffield, England, UK), (University of Texas, San Antonio, Texas, USA), ,
  • Formaat: 232 pages
  • Ilmumisaeg: 03-Oct-2018
  • Kirjastus: CRC Press Inc
  • Keel: eng
  • ISBN-13: 9781351835107
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Robust Control Systems with Genetic AlgorithmsSuurem pilt
  • Formaat: 232 pages
  • Ilmumisaeg: 03-Oct-2018
  • Kirjastus: CRC Press Inc
  • Keel: eng
  • ISBN-13: 9781351835107
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In recent years, new paradigms have emerged to replace-or augment-the traditional, mathematically based approaches to optimization. The most powerful of these are genetic algorithms (GA), inspired by natural selection, and genetic programming, an extension of GAs based on the optimization of symbolic codes.

Robust Control Systems with Genetic Algorithms builds a bridge between genetic algorithms and the design of robust control systems. After laying a foundation in the basics of GAs and genetic programming, it demonstrates the power of these new tools for developing optimal robust controllers for linear control systems, optimal disturbance rejection controllers, and predictive and variable structure control. It also explores the application of hybrid approaches: how to enhance genetic algorithms and programming with fuzzy logic to design intelligent control systems. The authors consider a variety of applications, such as the optimal control of robotic manipulators, flexible links and jet engines, and illustrate a multi-objective, genetic algorithm approach to the design of robust controllers with a gasification plant case study.

The authors are all masters in the field and clearly show the effectiveness of GA techniques. Their presentation is your first opportunity to fully explore this cutting-edge approach to robust optimal control system design and exploit its methods for your own applications.
Genetic algorithms
1(18)
Introduction to genetic algorithms
1(1)
Terms and definitions
2(2)
Representation
4(1)
Genetic algorithms with binary representation
4(1)
Genetic algorithms with real representation
4(1)
Fitness function
5(2)
Genetic operators
7(4)
Selection
7(1)
Proportionate Selection
7(1)
Tournament selection
8(1)
Crossover
8(1)
Crossover for binary representation
9(1)
Crossover for real representation
9(1)
Mutation
10(1)
Mutation for binary representation
10(1)
Mutation for real representation
10(1)
Genetic algorithms for optimization
11(2)
Genetic algorithms at work
11(1)
An optimization example
12(1)
Genetic programming
13(2)
Conclusions
15(4)
References
16(3)
Optimal robust control
19(20)
Introduction to the control theory
19(3)
Norms of signals and functions
22(1)
Description of model uncertainty
23(2)
Robust stability and disturbance rejection
25(4)
Condition for robust stability
25(2)
Condition for disturbance rejection
27(2)
Controller design
29(4)
Optimal controller design
30(2)
Optimal robust controller design
32(1)
Optimal disturbance rejection controller design
33(1)
Optimization
33(3)
The optimization problem
33(1)
Constraint handling
34(2)
Conclusions
36(3)
References
37(2)
Methods for controller design using genetic algorithms
39(22)
Introduction to controller design using genetic algorithms
39(1)
Design of optimal robust controller with fixed structure
39(9)
Design method
41(1)
Design example
42(6)
Design of optimal disturbance rejection controller with fixed structure
48(10)
Design method
50(1)
Design example
51(7)
Evaluation of the methods
58(1)
Conclusions
59(2)
References
59(2)
Predictive and variable structure control designs
61(18)
Model-based predictive controllers
61(7)
Basic concepts and algorithms
62(1)
Generalized predictive control
63(1)
Formulation and design of GPC
63(4)
Overview of optimization of GPC design by genetic algorithms
67(1)
Variable structure control systems
68(7)
Introduction
68(3)
Basic concepts and controller design
71(3)
Overview of optimization of variable structure control design by genetic algorithms
74(1)
References
75(4)
Design methods, simulation results, and conclusion
79(20)
Optimization of generalized predictive control design by genetic algorithms
79(16)
Design method
79(1)
Design example
80(2)
Simulation results
82(1)
Case study 1: Adaptive GPC design without constraints
82(1)
Case study 2: Adaptive GPC design with constraints for the control signal
82(3)
Optimization of quasi-sliding mode control design by genetic algorithms
85(1)
Design method
85(1)
Design example
86(2)
Simulation results
88(1)
Case study 1: Self-tuning quasi-sliding mode control
88(3)
Case study 2: Self-tuning quasi-sliding mode control with gain scheduling
91(4)
Conclusions
95(4)
References
97(2)
Tuning fuzzy logic controllers for robust control system design
99(20)
Introduction
99(1)
Fuzzy control
100(1)
Genetic tuning of fuzzy control systems
101(2)
Gas turbine engine control
103(4)
Gas turbine engines --- an overview
103(1)
GTE types
104(1)
The GTE control problem
105(2)
Fuzzy control system design --- example study
107(7)
Problem formulation
107(1)
Heuristic design of the fuzzy controllers
108(4)
GA tuning of the fuzzy controllers
112(2)
Applications of GAs for fuzzy control
114(5)
References
116(3)
GA-fuzzy hierarchical control design approach
119(18)
Introduction
119(3)
Hierarchical fuzzy control for a flexible robotic link
122(5)
A mathematical model
122(2)
Separation of spatial and temporal parameters
124(1)
The second level of hierarchical controller
124(1)
Line-curvature analysis
125(1)
The rule base
125(1)
The lower level of hierarchy
126(1)
Genetic algorithms in knowledge enhancement
127(3)
Interpretation function
127(1)
Incorporating initial knowledge from one expert
128(2)
Incorporating initial knowledge from several experts
130(1)
Implementation issues
130(2)
Software aspects
130(1)
Hardware aspects
131(1)
Simulation
132(2)
Conclusions
134(3)
References
135(2)
Autonomous robot navigation through fuzzy-genetic programming
137(16)
Introduction
137(1)
Hierarchical fuzzy-behavior control
138(3)
Behavior hierarchy
139(2)
Coordination by behavior modulation
141(2)
Related work
142(1)
Genetic programming of fuzzy behaviors
143(1)
Rule discovery
143(1)
Evolution of coordination
144(1)
Behavior fitness evaluation
144(1)
Autonomous navigation results
145(5)
Hand-derived behavior
146(2)
Evolved behavior
148(2)
Conclusions
150(3)
References
151(2)
Robust control system design: A hybrid H-infinity/multiobjective optimization approach
153(18)
Introduction
153(1)
H-infinity design of robust control systems
154(4)
Introduction to H-infinity design
154(1)
Loop-shaping design procedure
155(2)
H-infinity robust stabilization
157(1)
Multiobjective optimization
158(4)
Introduction to multiobjective optimization
158(1)
Multiobjective genetic algorithms
159(2)
Robust control system design: Incorporating multiobjective with H-infinity
161(1)
Case study: Robust control of a gasification plant
162(7)
Plant model and design requirements
163(1)
Problem formulation
164(1)
Design using a hybrid H-infinity/multiobjective optimization approach
165(4)
Conclusions
169(2)
References
170(1)
Appendix A Fuzzy sets, logic and control 171(32)
A.1 Introduction
171(1)
A.2 Classical sets
172(1)
A.3 Classical set operations
173(1)
A.4 Properties of classical sets
174(1)
A.5 Fuzzy sets and membership functions
175(2)
A.6 Fuzzy sets operations
177(2)
A.7 Properties of fuzzy sets
179(4)
A.8 Predicate logic
183(7)
A.9 Fuzzy logic
190(2)
A.10 Fuzzy control
192(2)
A.11 Basic definitions
194(8)
A.12 Conclusion
202(1)
References
202(1)
Index 203


Jamshidi, Mo; Krohling, Renato A.; dos S. Coelho, Leandro; Fleming, Peter J.
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