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Robust Control Systems with Genetic Algorithms

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kr 103

Robust Control Systems with Genetic Algorithms

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.
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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.
Produktdetaljer
Sprog: Engelsk
Sider: 232
ISBN-13: 9780367395728
Indbinding: Paperback
Udgave:
ISBN-10: 036739572X
Udg. Dato: 19 sep 2019
Længde: 0mm
Bredde: 234mm
Højde: 156mm
Forlag: Taylor & Francis Ltd
Oplagsdato: 19 sep 2019
Forfatter(e) Peter J. Fleming, Leandro dos S. Coelho, Mo Jamshidi, Renato A. Krohling


Kategori Automatisk styrings- & reguleringsteknik


Sprog Engelsk


Indbinding Paperback


Sider 232


Udgave


Længde 0mm


Bredde 234mm


Højde 156mm


Udg. Dato 19 sep 2019


Oplagsdato 19 sep 2019

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