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Robust Control Systems with Genetic Algorithms
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Robust Control Systems with Genetic Algorithms

Book Details

Format Paperback / Softback
ISBN-10 036739572X
ISBN-13 9780367395728
Publisher Taylor & Francis Ltd
Imprint CRC Press
Country of Manufacture GB
Country of Publication GB
Publication Date Sep 19th, 2019
Print length 232 Pages
Weight 453 grams
Ksh 12,600.00
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This text links genetic algorithms (GAs) 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 tools for developing optimal robust controllers for linear control systems, optimal disturbance rejection controllers, and predictive and variable structure control. It also explores the use of hybrid approaches that incorporate fuzzy logic into designs for intelligent control systems. The authors consider a variety of applications, such as robotic manipulators, flexible links, and jet engines, and illustrate a multi-objective GA approach using a case study.
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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