國立高雄大學圖資館 |

Language: English

Back

Decentralized neural controlapplicat...

Garcia-Hernandez, Ramon.

Decentralized neural controlapplication to robotics /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Decentralized neural controlby Ramon Garcia-Hernandez ... [et al.].
Reminder of title:	application to robotics /
other author:	Garcia-Hernandez, Ramon.
Published:	Cham :Springer International Publishing :2017.
Description:	xv, 111 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Neural networks (Computer science)
Online resource:	http://dx.doi.org/10.1007/978-3-319-53312-4
ISBN:	9783319533124$q(electronic bk.)

Decentralized neural controlapplication to robotics /
Decentralized neural controlapplication to robotics /[electronic resource] :by Ramon Garcia-Hernandez ... [et al.]. - Cham :Springer International Publishing :2017. - xv, 111 p. :ill., digital ;24 cm. - Studies in systems, decision and control,v.962198-4182 ;. - Studies in systems, decision and control ;v.3..

Introduction -- Foundations -- Decentralized Neural Block Control -- Decentralized Neural Backstepping Control -- Decentralized Inverse Optimal Control for Stabilization: a CLF Approach -- Decentralized Inverse Optimal Control for Trajectory Tracking -- Robotics Application -- Conclusions.

This book provides a decentralized approach for the identification and control of robotics systems. It also presents recent research in decentralized neural control and includes applications to robotics. Decentralized control is free from difficulties due to complexity in design, debugging, data gathering and storage requirements, making it preferable for interconnected systems. Furthermore, as opposed to the centralized approach, it can be implemented with parallel processors. This approach deals with four decentralized control schemes, which are able to identify the robot dynamics. The training of each neural network is performed on-line using an extended Kalman filter (EKF) The first indirect decentralized control scheme applies the discrete-time block control approach, to formulate a nonlinear sliding manifold. The second direct decentralized neural control scheme is based on the backstepping technique, approximated by a high order neural network. The third control scheme applies a decentralized neural inverse optimal control for stabilization. The fourth decentralized neural inverse optimal control is designed for trajectory tracking. This comprehensive work on decentralized control of robot manipulators and mobile robots is intended for professors, students and professionals wanting to understand and apply advanced knowledge in their field of work.

ISBN: 9783319533124$q(electronic bk.)

Standard No.: 10.1007/978-3-319-53312-4doiSubjects--Topical Terms:

181982
Neural networks (Computer science)

LC Class. No.: QA76.87

Dewey Class. No.: 006.32

Decentralized neural controlapplication to robotics /
LDR:02684nmm a2200325 a 4500 001 507446
003 DE-He213
005 20170823161523.0
006 m d
007 cr nn 008maaau
008 171030s2017 gw s 0 eng d
020 $a 9783319533124$q(electronic bk.)
020 $a 9783319533117$q(paper)
024 7 $a 10.1007/978-3-319-53312-4 $2 doi
035 $a 978-3-319-53312-4
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.87
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
082 0 4 $a 006.32 $2 23
090 $a QA76.87 $b .D292 2017
245 0 0 $a Decentralized neural control $h [electronic resource] : $b application to robotics / $c by Ramon Garcia-Hernandez ... [et al.].
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2017.
300 $a xv, 111 p. : $b ill., digital ; $c 24 cm.
490 1 $a Studies in systems, decision and control, $x 2198-4182 ; $v v.96
505 0 $a Introduction -- Foundations -- Decentralized Neural Block Control -- Decentralized Neural Backstepping Control -- Decentralized Inverse Optimal Control for Stabilization: a CLF Approach -- Decentralized Inverse Optimal Control for Trajectory Tracking -- Robotics Application -- Conclusions.
520 $a This book provides a decentralized approach for the identification and control of robotics systems. It also presents recent research in decentralized neural control and includes applications to robotics. Decentralized control is free from difficulties due to complexity in design, debugging, data gathering and storage requirements, making it preferable for interconnected systems. Furthermore, as opposed to the centralized approach, it can be implemented with parallel processors. This approach deals with four decentralized control schemes, which are able to identify the robot dynamics. The training of each neural network is performed on-line using an extended Kalman filter (EKF) The first indirect decentralized control scheme applies the discrete-time block control approach, to formulate a nonlinear sliding manifold. The second direct decentralized neural control scheme is based on the backstepping technique, approximated by a high order neural network. The third control scheme applies a decentralized neural inverse optimal control for stabilization. The fourth decentralized neural inverse optimal control is designed for trajectory tracking. This comprehensive work on decentralized control of robot manipulators and mobile robots is intended for professors, students and professionals wanting to understand and apply advanced knowledge in their field of work.
650 0 $a Neural networks (Computer science) $3 181982
650 0 $a Nonlinear control theory. $3 182079
650 1 4 $a Engineering. $3 210888
650 2 4 $a Computational Intelligence. $3 338479
650 2 4 $a Control. $3 349080
650 2 4 $a Robotics and Automation. $3 357111
700 1 $a Garcia-Hernandez, Ramon. $3 774091
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
830 0 $a Studies in systems, decision and control ; $v v.3. $3 678532
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-53312-4
950 $a Engineering (Springer-11647)