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Adaptive Neural Network Control of Robotic Manipulators
Author: Tong Heng Lee
Publisher: World Scientific
Published: 1998
Total Pages: 400
ISBN-13: 9789810234522
DOWNLOAD EBOOK →Introduction; Mathematical background; Dynamic modelling of robots; Structured network modelling of robots; Adaptive neural network control of robots; Neural network model reference adaptive control; Flexible joint robots; task space and force control; Bibliography; Computer simulation; Simulation software in C.
Neural Network Control Of Robot Manipulators And Non-Linear Systems
Author: F W Lewis
Publisher: CRC Press
Published: 2020-08-14
Total Pages: 468
ISBN-13: 100016277X
DOWNLOAD EBOOK →There has been great interest in "universal controllers" that mimic the functions of human processes to learn about the systems they are controlling on-line so that performance improves automatically. Neural network controllers are derived for robot manipulators in a variety of applications including position control, force control, link flexibility stabilization and the management of high-frequency joint and motor dynamics. The first chapter provides a background on neural networks and the second on dynamical systems and control. Chapter three introduces the robot control problem and standard techniques such as torque, adaptive and robust control. Subsequent chapters give design techniques and Stability Proofs For NN Controllers For Robot Arms, Practical Robotic systems with high frequency vibratory modes, force control and a general class of non-linear systems. The last chapters are devoted to discrete- time NN controllers. Throughout the text, worked examples are provided.
Adaptive Neural Network Control of Robotic Manipulators
Author: Shuzhi S. Ge
Publisher: World Scientific Series In Robotics And Intelligent Systems
Published: 1998
Total Pages: 381
ISBN-13: 9789810234522
DOWNLOAD EBOOK →Recently, there has been considerable research interest in neural network control of robots, and satisfactory results have been obtained in solving some of the special issues associated with the problems of robot control in an "on-and-off" fashion. This book is dedicated to issues on adaptive control of robots based on neural networks. The text has been carefully tailored to (i) give a comprehensive study of robot dynamics, (ii) present structured network models for robots, and (iii) provide systematic approaches for neural network based adaptive controller design for rigid robots, flexible joint robots, and robots in constraint motion. Rigorous proof of the stability properties of adaptive neural network controllers is provided. Simulation examples are also presented to verify the effectiveness of the controllers, and practical implementation issues associated with the controllers are also discussed.
Control of Robot Manipulators
Author: Frank L. Lewis
Publisher: MacMillan Publishing Company
Published: 1993
Total Pages: 450
ISBN-13:
DOWNLOAD EBOOK →Robot Manipulator Control
Author: Frank L. Lewis
Publisher: CRC Press
Published: 2003-12-12
Total Pages: 646
ISBN-13: 9780203026953
DOWNLOAD EBOOK →Robot Manipulator Control offers a complete survey of control systems for serial-link robot arms and acknowledges how robotic device performance hinges upon a well-developed control system. Containing over 750 essential equations, this thoroughly up-to-date Second Edition, the book explicates theoretical and mathematical requisites for controls design and summarizes current techniques in computer simulation and implementation of controllers. It also addresses procedures and issues in computed-torque, robust, adaptive, neural network, and force control. New chapters relay practical information on commercial robot manipulators and devices and cutting-edge methods in neural network control.
Adaptive Control of Robot Manipulators
Author: An-Chyau Huang
Publisher: World Scientific
Published: 2010
Total Pages: 274
ISBN-13: 9814307424
DOWNLOAD EBOOK →This book introduces an unified function approximation approach to the control of uncertain robot manipulators containing general uncertainties. It works for free space tracking control as well as compliant motion control. It is applicable to the rigid robot and the flexible joint robot. Even with actuator dynamics, the unified approach is still feasible. All these features make the book stand out from other existing publications.
Neural Networks for Robotic Control
Author: Ali M. S. Zalzala
Publisher: Prentice Hall
Published: 1996
Total Pages: 296
ISBN-13:
DOWNLOAD EBOOK →1. An overview of neural networks in control applications; 2. Artificial neural network based intelligent robot dynamic control; 3. Neural servo controller for position, force stabbing control of robotic manipulators; 4. Model-based adaptive neural structures for robotic control; 5. Intelligent co-ordination of multiple systems with neural networks; 6. Neural networks for mobile robot piloting control; 7. A neural network controller for the navigation and obstacle avoidance of a mobile robot; An ultrasonic 3-D robot vision system based on the statistical properties of artificial neural networks; Visual control of robotic manipulator based on neural networks; 10. Brain building for a biological robot; 11. Robustness of a distributed neural network controller for locomotion in a hexapod robot.
Adaptive Control for Robotic Manipulators
Author: Dan Zhang
Publisher: CRC Press
Published: 2017-02-03
Total Pages: 407
ISBN-13: 1351678922
DOWNLOAD EBOOK →The robotic mechanism and its controller make a complete system. As the robotic mechanism is reconfigured, the control system has to be adapted accordingly. The need for the reconfiguration usually arises from the changing functional requirements. This book will focus on the adaptive control of robotic manipulators to address the changed conditions. The aim of the book is to summarise and introduce the state-of-the-art technologies in the field of adaptive control of robotic manipulators in order to improve the methodologies on the adaptive control of robotic manipulators. Advances made in the past decades are described in the book, including adaptive control theories and design, and application of adaptive control to robotic manipulators.
Radial Basis Function (RBF) Neural Network Control for Mechanical Systems
Author: Jinkun Liu
Publisher: Springer Science & Business Media
Published: 2013-01-26
Total Pages: 375
ISBN-13: 3642348165
DOWNLOAD EBOOK →Radial Basis Function (RBF) Neural Network Control for Mechanical Systems is motivated by the need for systematic design approaches to stable adaptive control system design using neural network approximation-based techniques. The main objectives of the book are to introduce the concrete design methods and MATLAB simulation of stable adaptive RBF neural control strategies. In this book, a broad range of implementable neural network control design methods for mechanical systems are presented, such as robot manipulators, inverted pendulums, single link flexible joint robots, motors, etc. Advanced neural network controller design methods and their stability analysis are explored. The book provides readers with the fundamentals of neural network control system design. This book is intended for the researchers in the fields of neural adaptive control, mechanical systems, Matlab simulation, engineering design, robotics and automation. Jinkun Liu is a professor at Beijing University of Aeronautics and Astronautics.
