Fr. 69.00

Stochastic Optimal Control Theory with Application in Self-Tuning Control

English · Paperback / Softback

Shipping usually within 1 to 2 weeks (title will be printed to order)

Description

Read more

This book merges two major areas of control: the design of control systems and adaptive control. Original contributions are made in the polynomial approach to stochastic optimal control and the resulting control laws are then manipulated into a form suitable for application in the self-tuning control framework. A major contribution is the derivation of both scalar and multivariable optimal controllers for the rejection of measurable disturbances using feedforward. A powerful feature of the book is the presentation of a case-study in which the LQG self-tuner was tested on the pressure control loop of a power station. The broad coverage of the book should appeal not only to research workers, teachers and students of control engineering, but also to practicing industrial control engineers.

List of contents

to stochastic optimal control.- Stochastic tracking with measurable disturbance feedforward.- to self-tuning control.- Optimal self-tuning algorithm.- A power systems application.- Conclusions.

Product details

Authors Kenneth J Hunt, Kenneth J. Hunt
Publisher Springer, Berlin
 
Languages English
Product format Paperback / Softback
Released 15.10.2013
 
EAN 9783540505327
ISBN 978-3-540-50532-7
No. of pages 311
Dimensions 165 mm x 242 mm x 13 mm
Weight 560 g
Illustrations X, 311 p. 53 illus.
Series Lecture Notes in Control and Information Sciences
Lecture Notes in Control and Information Sciences
Subject Natural sciences, medicine, IT, technology > Technology > Electronics, electrical engineering, communications engineering

Customer reviews

No reviews have been written for this item yet. Write the first review and be helpful to other users when they decide on a purchase.

Write a review

Thumbs up or thumbs down? Write your own review.

For messages to CeDe.ch please use the contact form.

The input fields marked * are obligatory

By submitting this form you agree to our data privacy statement.