9781849965842-1849965846-Nonlinear H2/H-Infinity Constrained Feedback Control: A Practical Design Approach Using Neural Networks (Advances in Industrial Control)

Nonlinear H2/H-Infinity Constrained Feedback Control: A Practical Design Approach Using Neural Networks (Advances in Industrial Control)

ISBN-13: 9781849965842
ISBN-10: 1849965846
Edition: Softcover reprint of hardcover 1st ed. 2006
Author: Frank L. Lewis, Murad Abu-Khalaf, Jie Huang
Publication date: 2010
Publisher: Springer
Format: Paperback 224 pages
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Book details

ISBN-13: 9781849965842
ISBN-10: 1849965846
Edition: Softcover reprint of hardcover 1st ed. 2006
Author: Frank L. Lewis, Murad Abu-Khalaf, Jie Huang
Publication date: 2010
Publisher: Springer
Format: Paperback 224 pages

Summary

Nonlinear H2/H-Infinity Constrained Feedback Control: A Practical Design Approach Using Neural Networks (Advances in Industrial Control) (ISBN-13: 9781849965842 and ISBN-10: 1849965846), written by authors Frank L. Lewis, Murad Abu-Khalaf, Jie Huang, was published by Springer in 2010. With an overall rating of 3.5 stars, it's a notable title among other AI & Machine Learning (Robotics, Hardware & DIY, Automotive, Engineering, Chemical, Mechanical, Industrial & Technical, Chemistry, Computer Science) books. You can easily purchase or rent Nonlinear H2/H-Infinity Constrained Feedback Control: A Practical Design Approach Using Neural Networks (Advances in Industrial Control) (Paperback) from BooksRun, along with many other new and used AI & Machine Learning books and textbooks. And, if you're looking to sell your copy, our current buyback offer is $0.3.

Description

This book provides techniques to produce robust, stable and useable solutions to problems of H-infinity and H2 control in high-performance, non-linear systems for the first time. The book is of importance to control designers working in a variety of industrial systems. Case studies are given and the design of nonlinear control systems of the same caliber as those obtained in recent years using linear optimal and bounded-norm designs is explained.

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