Neural Network Control Of Robot Manipulators And Non Linear Systems

Author : F W Lewis,S. Jagannathan,A Yesildirak
Publisher : CRC Press
Page : 468 pages
File Size : 46,7 Mb
Release : 2020-08-14
Category : Technology & Engineering
ISBN : 9781000162776

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Neural Network Control Of Robot Manipulators And Non-Linear Systems by F W Lewis,S. Jagannathan,A Yesildirak Pdf

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.

Author : Anonim
Publisher : Unknown
Page : 128 pages
File Size : 54,9 Mb
Release : 2024-06-16
Category : Electronic
ISBN : 9789814496223

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Adaptive Neural Network Control of Robotic Manipulators by Anonim Pdf

Author : Tong Heng Lee,Christopher John Harris
Publisher : World Scientific
Page : 400 pages
File Size : 44,5 Mb
Release : 1998
Category : Electronic
ISBN : 981023452X

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Adaptive Neural Network Control of Robotic Manipulators by Tong Heng Lee,Christopher John Harris Pdf

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.

Author : Alexander S. Poznyak,Edgar N. Sanchez,Wen Yu (profesor titular.)
Publisher : World Scientific
Page : 464 pages
File Size : 42,8 Mb
Release : 2001
Category : Science
ISBN : 981281129X

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Differential Neural Networks for Robust Nonlinear Control by Alexander S. Poznyak,Edgar N. Sanchez,Wen Yu (profesor titular.) Pdf

This book deals with continuous time dynamic neural networks theory applied to the solution of basic problems in robust control theory, including identification, state space estimation (based on neuro-observers) and trajectory tracking. The plants to be identified and controlled are assumed to be a priori unknown but belonging to a given class containing internal unmodelled dynamics and external perturbations as well. The error stability analysis and the corresponding error bounds for different problems are presented. The effectiveness of the suggested approach is illustrated by its application to various controlled physical systems (robotic, chaotic, chemical, etc.). Contents: Theoretical Study: Neural Networks Structures; Nonlinear System Identification: Differential Learning; Sliding Mode Identification: Algebraic Learning; Neural State Estimation; Passivation via Neuro Control; Neuro Trajectory Tracking; Neurocontrol Applications: Neural Control for Chaos; Neuro Control for Robot Manipulators; Identification of Chemical Processes; Neuro Control for Distillation Column; General Conclusions and Future Work; Appendices: Some Useful Mathematical Facts; Elements of Qualitative Theory of ODE; Locally Optimal Control and Optimization. Readership: Graduate students, researchers, academics/lecturers and industrialists in neural networks.

Author : Ramon Garcia-Hernandez,Michel Lopez-Franco,Edgar N. Sanchez,Alma y. Alanis,Jose A. Ruz-Hernandez
Publisher : Springer
Page : 111 pages
File Size : 44,8 Mb
Release : 2017-02-05
Category : Technology & Engineering
ISBN : 9783319533124

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Decentralized Neural Control: Application to Robotics by Ramon Garcia-Hernandez,Michel Lopez-Franco,Edgar N. Sanchez,Alma y. Alanis,Jose A. Ruz-Hernandez Pdf

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.

Author : Frank L. Lewis,Darren M. Dawson,Chaouki T. Abdallah
Publisher : CRC Press
Page : 646 pages
File Size : 47,8 Mb
Release : 2003-12-12
Category : Technology & Engineering
ISBN : 0203026950

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Robot Manipulator Control by Frank L. Lewis,Darren M. Dawson,Chaouki T. Abdallah Pdf

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.

Author : Jagannathan Sarangapani
Publisher : CRC Press
Page : 624 pages
File Size : 49,6 Mb
Release : 2018-10-03
Category : Technology & Engineering
ISBN : 9781420015454

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Neural Network Control of Nonlinear Discrete-Time Systems by Jagannathan Sarangapani Pdf

Intelligent systems are a hallmark of modern feedback control systems. But as these systems mature, we have come to expect higher levels of performance in speed and accuracy in the face of severe nonlinearities, disturbances, unforeseen dynamics, and unstructured uncertainties. Artificial neural networks offer a combination of adaptability, parallel processing, and learning capabilities that outperform other intelligent control methods in more complex systems. Borrowing from Biology Examining neurocontroller design in discrete-time for the first time, Neural Network Control of Nonlinear Discrete-Time Systems presents powerful modern control techniques based on the parallelism and adaptive capabilities of biological nervous systems. At every step, the author derives rigorous stability proofs and presents simulation examples to demonstrate the concepts. Progressive Development After an introduction to neural networks, dynamical systems, control of nonlinear systems, and feedback linearization, the book builds systematically from actuator nonlinearities and strict feedback in nonlinear systems to nonstrict feedback, system identification, model reference adaptive control, and novel optimal control using the Hamilton-Jacobi-Bellman formulation. The author concludes by developing a framework for implementing intelligent control in actual industrial systems using embedded hardware. Neural Network Control of Nonlinear Discrete-Time Systems fosters an understanding of neural network controllers and explains how to build them using detailed derivations, stability analysis, and computer simulations.

Author : Young Ho Kim,Frank L Lewis
Publisher : World Scientific
Page : 228 pages
File Size : 52,8 Mb
Release : 1998-09-28
Category : Technology & Engineering
ISBN : 9789814496452

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High-level Feedback Control With Neural Networks by Young Ho Kim,Frank L Lewis Pdf

Complex industrial or robotic systems with uncertainty and disturbances are difficult to control. As system uncertainty or performance requirements increase, it becomes necessary to augment traditional feedback controllers with additional feedback loops that effectively “add intelligence” to the system. Some theories of artificial intelligence (AI) are now showing how complex machine systems should mimic human cognitive and biological processes to improve their capabilities for dealing with uncertainty.This book bridges the gap between feedback control and AI. It provides design techniques for “high-level” neural-network feedback-control topologies that contain servo-level feedback-control loops as well as AI decision and training at the higher levels. Several advanced feedback topologies containing neural networks are presented, including “dynamic output feedback”, “reinforcement learning” and “optimal design”, as well as a “fuzzy-logic reinforcement” controller. The control topologies are intuitive, yet are derived using sound mathematical principles where proofs of stability are given so that closed-loop performance can be relied upon in using these control systems. Computer-simulation examples are given to illustrate the performance.

Author : Agustin Jimenez,Basil M. Al Hadithi
Publisher : BoD – Books on Demand
Page : 680 pages
File Size : 42,7 Mb
Release : 2010-03-01
Category : Technology & Engineering
ISBN : 9789533070735

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Robot Manipulators by Agustin Jimenez,Basil M. Al Hadithi Pdf

This book presents the most recent research advances in robot manipulators. It offers a complete survey to the kinematic and dynamic modelling, simulation, computer vision, software engineering, optimization and design of control algorithms applied for robotic systems. It is devoted for a large scale of applications, such as manufacturing, manipulation, medicine and automation. Several control methods are included such as optimal, adaptive, robust, force, fuzzy and neural network control strategies. The trajectory planning is discussed in details for point-to-point and path motions control. The results in obtained in this book are expected to be of great interest for researchers, engineers, scientists and students, in engineering studies and industrial sectors related to robot modelling, design, control, and application. The book also details theoretical, mathematical and practical requirements for mathematicians and control engineers. It surveys recent techniques in modelling, computer simulation and implementation of advanced and intelligent controllers.

Author : Ali M. S. Zalzala,Alan S. Morris
Publisher : Prentice Hall
Page : 296 pages
File Size : 51,5 Mb
Release : 1996
Category : Computers
ISBN : UOM:39015038422682

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Neural Networks for Robotic Control by Ali M. S. Zalzala,Alan S. Morris Pdf

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.

Author : Yunong Zhang,Zhijun Zhang
Publisher : Springer Science & Business Media
Page : 196 pages
File Size : 55,9 Mb
Release : 2014-07-08
Category : Technology & Engineering
ISBN : 9783642375187

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Repetitive Motion Planning and Control of Redundant Robot Manipulators by Yunong Zhang,Zhijun Zhang Pdf

Repetitive Motion Planning and Control of Redundant Robot Manipulators presents four typical motion planning schemes based on optimization techniques, including the fundamental RMP scheme and its extensions. These schemes are unified as quadratic programs (QPs), which are solved by neural networks or numerical algorithms. The RMP schemes are demonstrated effectively by the simulation results based on various robotic models; the experiments applying the fundamental RMP scheme to a physical robot manipulator are also presented. As the schemes and the corresponding solvers presented in the book have solved the non-repetitive motion problems existing in redundant robot manipulators, it is of particular use in applying theoretical research based on the quadratic program for redundant robot manipulators in industrial situations. This book will be a valuable reference work for engineers, researchers, advanced undergraduate and graduate students in robotics fields. Yunong Zhang is a professor at The School of Information Science and Technology, Sun Yat-sen University, Guangzhou, China; Zhijun Zhang is a research fellow working at the same institute.

Author : Mahmoud Al Ashi
Publisher : LAP Lambert Academic Publishing
Page : 100 pages
File Size : 54,7 Mb
Release : 2014-04-17
Category : Electronic
ISBN : 365928968X

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Robotic Manipulator Control Using Neural Networks by Mahmoud Al Ashi Pdf

The learning capabilities of artificial neural networks (ANNs) to identify and emulate the behavior of complicated nonlinear systems have made them effective tools that can be utilized in intelligent adaptive control strategies. The use of ANNs in the design of trajectory tracking controllers for robotic manipulators is dated back to the 1980s. Due to the flexibility of their structure as well as the continuous development and enhancement of their self-training algorithms, the use of ANNs in the field of robotic manipulator trajectory tracking control is being considered an important research area. This textbook explains in great detail the process of designing an effective controller to enhance the trajectory tracking performance of a two degree of freedom (2-DOF) robotic arm using neural networks. Feed-forward ANNs were used in both model-based and non-model-based control strategies. Since it also includes a deep explanation of the modeling of the 2-DOF robotic arm system including its actuating DC-motors and their control using a PD controller, this textbook can also serve as an effective educational tool for both undergraduate and graduate electrical engineering students.

Author : Heidar A. Talebi,Farzaneh Abdollahi,Rajni V. Patel,Khashayar Khorasani
Publisher : Springer
Page : 154 pages
File Size : 44,6 Mb
Release : 2009-12-04
Category : Technology & Engineering
ISBN : 9781441914385

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Neural Network-Based State Estimation of Nonlinear Systems by Heidar A. Talebi,Farzaneh Abdollahi,Rajni V. Patel,Khashayar Khorasani Pdf

"Neural Network-Based State Estimation of Nonlinear Systems" presents efficient, easy to implement neural network schemes for state estimation, system identification, and fault detection and Isolation with mathematical proof of stability, experimental evaluation, and Robustness against unmolded dynamics, external disturbances, and measurement noises.

Author : Zhang, Ming
Publisher : IGI Global
Page : 511 pages
File Size : 51,9 Mb
Release : 2016-05-05
Category : Computers
ISBN : 9781522500643

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Applied Artificial Higher Order Neural Networks for Control and Recognition by Zhang, Ming Pdf

In recent years, Higher Order Neural Networks (HONNs) have been widely adopted by researchers for applications in control signal generating, pattern recognition, nonlinear recognition, classification, and predition of control and recognition scenarios. Due to the fact that HONNs have been proven to be faster, more accurate, and easier to explain than traditional neural networks, their applications are limitless. Applied Artificial Higher Order Neural Networks for Control and Recognition explores the ways in which higher order neural networks are being integrated specifically for intelligent technology applications. Emphasizing emerging research, practice, and real-world implementation, this timely reference publication is an essential reference source for researchers, IT professionals, and graduate-level computer science and engineering students.

Author : Dan Zhang,Bin Wei
Publisher : CRC Press
Page : 407 pages
File Size : 53,8 Mb
Release : 2017-02-03
Category : Science
ISBN : 9781351678926

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Adaptive Control for Robotic Manipulators by Dan Zhang,Bin Wei Pdf

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.