Decentralized Neural Control: Application to Robotics
Decentralized Neural Control: Application to Robotics

Author: Ramon Garcia-Hernandez

Publisher: Springer

Published: 2017-02-05

Total Pages: 121

ISBN-13: 3319533126

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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.

Control and Dynamic Systems V40: Advances in Robotic Systems Part 2 of 2
Control and Dynamic Systems V40: Advances in Robotic Systems Part 2 of 2

Author: C.T. Leonides

Publisher: Academic Press

Published: 2012-12-02

Total Pages: 432

ISBN-13: 0323162886

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Advances in Robotic Systems, Part 2 is the second of a companion set of two volumes on advances in robotic systems dynamics and control. This book comprises nine chapters, with the first focusing on kinesthetic feedback techniques in teleoperated systems. The succeeding chapters then delve into topics such as parallel algorithms and fault-tolerant reconfigurable architecture for robot kinematics and dynamics computations; trajectory planning for robot control; and a control systems perspective. Other chapters cover simplified techniques for adaptive control of robotic systems; theory and applications of configuration control for redundant manipulators; nonlinear feedback for force control of robot manipulators; systolic architectures for dynamic control of manipulators; inverse dynamics; and forward dynamics. This book will be of interest to practitioners in the fields of computer science, systems science, and mathematics.

Decentralized Control and Decentralized Adaptive Control
Decentralized Control and Decentralized Adaptive Control

Author:

Publisher:

Published: 2006

Total Pages: 31

ISBN-13:

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As systems become complex with many interconnected subsystems, decentralized control becomes essential. When certain parameters of the system are unknown, and/or when subsystems are not aware of the signals from other subsystems that affect their behavior, we need decentralized adaptive control. The report deals with questions that arise while analyzing the stability and performance of decentralized adaptive control systems. The project produced three specific results: 1. Interconnected dynamical systems can be stable even when there is no communication between subsystems, provided all subsystems have common knowledge of the goals of the other subsystems. 2. Even though stability can be achieved without communication, the latter is necessary to satisfy performance requirements. To keep communication costs to a minimum, partial communication has to be used. This gives rise to stability problems which were resolved. 3. The problem as to when a subsystem in an interconnected-system communicates with another is an important one and needs to be investigated further. Simulation results have clearly shown that significant improvement in the performance of the overall system can be achieved by subsystems communicating only over critical intervals of time.