Control of Flexible-link Manipulators Using Neural Networks

H. A. Talebi

ISBN 10: 1852334096 / ISBN 13: 9781852334093

Published by Springer London, 2001

About this Item

Druck auf Anfrage Neuware - Printed after ordering - Control of Flexible-link Manipulators Using Neural Networks addresses the difficulties that arise in controlling the end-point of a manipulator that has a significant amount of structural flexibility in its links. The non-minimum phase characteristic, coupling effects, nonlinearities, parameter variations and unmodeled dynamics in such a manipulator all contribute to these difficulties. Control strategies that ignore these uncertainties and nonlinearities generally fail to provide satisfactory closed-loop performance. This monograph develops and experimentally evaluates several intelligent (neural network based) control techniques to address the problem of controlling the end-point of flexible-link manipulators in the presence of all the aforementioned difficulties. To highlight the main issues, a very flexible-link manipulator whose hub exhibits a considerable amount of friction is considered for the experimental work. Four different neural network schemes are proposed and implemented on the experimental test-bed. The neural networks are trained and employed as online controllers.

Seller Inventory # 9781852334093

Bibliographic Details

• Title: Control of Flexible-link Manipulators Using Neural Networks
• Publisher: Springer London
• Publication Date: 2001
• Language: English
• ISBN 10: 1852334096
• ISBN 13: 9781852334093
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 233W x 155H x 10D millimeters 233 millimeters width by 155 millimeters height by 10 millimeters depth
• Weight: 263 grams

About this title

Synopsis

Control of Flexible-link Manipulators Using Neural Networks addresses the difficulties that arise in controlling the end-point of a manipulator that has a significant amount of structural flexibility in its links. The non-minimum phase characteristic, coupling effects, nonlinearities, parameter variations and unmodeled dynamics in such a manipulator all contribute to these difficulties. Control strategies that ignore these uncertainties and nonlinearities generally fail to provide satisfactory closed-loop performance. This monograph develops and experimentally evaluates several intelligent (neural network based) control techniques to address the problem of controlling the end-point of flexible-link manipulators in the presence of all the aforementioned difficulties. To highlight the main issues, a very flexible-link manipulator whose hub exhibits a considerable amount of friction is considered for the experimental work. Four different neural network schemes are proposed and implemented on the experimental test-bed. The neural networks are trained and employed as online controllers.