Decentralized Neural Control: Application to Robotics

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Beschreibung

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.


Inhalt

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.

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Produktinformationen

Titel
Decentralized Neural Control: Application to Robotics
Autor
EAN
9783319533117
ISBN
978-3-319-53311-7
Format
Fester Einband
Herausgeber
Springer, Berlin
Genre
Technik
Anzahl Seiten
111
Gewicht
349g
Größe
H13mm x B242mm x T157mm
Jahr
2017
Untertitel
Englisch
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