Adaptive Neural Network Based Target Tracking : Adaptive Estimation For Control Of Uncertain Nonlinear Systems With Applications To Target Tracking

Venkatesh Madyastha

ISBN 10: 3639166949 / ISBN 13: 9783639166941

Published by VDM Verlag Dr. Müller, 2009

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - Design of nonlinear observers has received considerable attention since the early development of methods for state estimation. The most popular approach is the extended Kalman filter (EKF) that goes through significant degradation in the presence of unmodeled nonlinearities. For uncertain nonlinear systems, adaptive observers have been introduced to estimate the unknown parameters where no apriori information about the unknown parameters is available. While establishing global results, these approaches are only applicable to systems transformable to output feedback form. Over the recent years, neural network (NN) based identification and estimation schemes have been proposed that relax the assumptions on the system at the price of sacrificing on the global nature of the results. However, most of the NN based adaptive observers in the literature require knowledge of the full dimension of the system, therefore may not be suitable for systems with unmodeled dynamics. A novel approach to nonlinear state estimation, robust to unmodeled dynamics, is proposed from the perspective of augmenting an EKF with an NN based adaptive element.

Seller Inventory # 9783639166941

Bibliographic Details

• Title: Adaptive Neural Network Based Target Tracking : Adaptive Estimation For Control Of Uncertain Nonlinear Systems With Applications To Target Tracking
• Publisher: VDM Verlag Dr. Müller
• Publication Date: 2009
• Language: English
• ISBN 10: 3639166949
• ISBN 13: 9783639166941
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 176H x 33D millimeters 220 millimeters width by 176 millimeters height by 33 millimeters depth
• Weight: 308 grams

About this title

Synopsis

Design of nonlinear observers has received considerable attention since the early development of methods for state estimation. The most popular approach is the extended Kalman filter (EKF) that goes through significant degradation in the presence of unmodeled nonlinearities. For uncertain nonlinear systems, adaptive observers have been introduced to estimate the unknown parameters where no apriori information about the unknown parameters is available. While establishing global results, these approaches are only applicable to systems transformable to output feedback form. Over the recent years, neural network (NN) based identification and estimation schemes have been proposed that relax the assumptions on the system at the price of sacrificing on the global nature of the results. However, most of the NN based adaptive observers in the literature require knowledge of the full dimension of the system, therefore may not be suitable for systems with unmodeled dynamics. A novel approach to nonlinear state estimation, robust to unmodeled dynamics, is proposed from the perspective of augmenting an EKF with an NN based adaptive element.

About the Author

Venky Madyastha obtained his doctoral degree in the year 2005 from the school of aerospace engineering, Georgia Institute of Technology, USA. His doctoral research focussed on adaptive nonlinear state estimation for control of uncertain nonlinear systems. He is currently with the General Electric Global Research Center, Bangalore, India.