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Author: Shuai Li Publisher: John Wiley & Sons ISBN: 1119556996 Category : Technology & Engineering Languages : en Pages : 278
Book Description
Presents pioneering and comprehensive work on engaging movement in robotic arms, with a specific focus on neural networks This book presents and investigates different methods and schemes for the control of robotic arms whilst exploring the field from all angles. On a more specific level, it deals with the dynamic-neural-network based kinematic control of redundant robot arms by using theoretical tools and simulations. Kinematic Control of Redundant Robot Arms Using Neural Networks is divided into three parts: Neural Networks for Serial Robot Arm Control; Neural Networks for Parallel Robot Control; and Neural Networks for Cooperative Control. The book starts by covering zeroing neural networks for control, and follows up with chapters on adaptive dynamic programming neural networks for control; projection neural networks for robot arm control; and neural learning and control co-design for robot arm control. Next, it looks at robust neural controller design for robot arm control and teaches readers how to use neural networks to avoid robot singularity. It then instructs on neural network based Stewart platform control and neural network based learning and control co-design for Stewart platform control. The book finishes with a section on zeroing neural networks for robot arm motion generation. Provides comprehensive understanding on robot arm control aided with neural networks Presents neural network-based control techniques for single robot arms, parallel robot arms (Stewart platforms), and cooperative robot arms Provides a comparison of, and the advantages of, using neural networks for control purposes rather than traditional control based methods Includes simulation and modelling tasks (e.g., MATLAB) for onward application for research and engineering development By focusing on robot arm control aided by neural networks whilst examining central topics surrounding the field, Kinematic Control of Redundant Robot Arms Using Neural Networks is an excellent book for graduate students and academic and industrial researchers studying neural dynamics, neural networks, analog and digital circuits, mechatronics, and mechanical engineering.
Author: Shuai Li Publisher: John Wiley & Sons ISBN: 1119556996 Category : Technology & Engineering Languages : en Pages : 278
Book Description
Presents pioneering and comprehensive work on engaging movement in robotic arms, with a specific focus on neural networks This book presents and investigates different methods and schemes for the control of robotic arms whilst exploring the field from all angles. On a more specific level, it deals with the dynamic-neural-network based kinematic control of redundant robot arms by using theoretical tools and simulations. Kinematic Control of Redundant Robot Arms Using Neural Networks is divided into three parts: Neural Networks for Serial Robot Arm Control; Neural Networks for Parallel Robot Control; and Neural Networks for Cooperative Control. The book starts by covering zeroing neural networks for control, and follows up with chapters on adaptive dynamic programming neural networks for control; projection neural networks for robot arm control; and neural learning and control co-design for robot arm control. Next, it looks at robust neural controller design for robot arm control and teaches readers how to use neural networks to avoid robot singularity. It then instructs on neural network based Stewart platform control and neural network based learning and control co-design for Stewart platform control. The book finishes with a section on zeroing neural networks for robot arm motion generation. Provides comprehensive understanding on robot arm control aided with neural networks Presents neural network-based control techniques for single robot arms, parallel robot arms (Stewart platforms), and cooperative robot arms Provides a comparison of, and the advantages of, using neural networks for control purposes rather than traditional control based methods Includes simulation and modelling tasks (e.g., MATLAB) for onward application for research and engineering development By focusing on robot arm control aided by neural networks whilst examining central topics surrounding the field, Kinematic Control of Redundant Robot Arms Using Neural Networks is an excellent book for graduate students and academic and industrial researchers studying neural dynamics, neural networks, analog and digital circuits, mechatronics, and mechanical engineering.
Author: Shuai Li Publisher: Springer ISBN: 9811070377 Category : Technology & Engineering Languages : en Pages : 74
Book Description
This is the first book to focus on solving cooperative control problems of multiple robot arms using different centralized or distributed neural network models, presenting methods and algorithms together with the corresponding theoretical analysis and simulated examples. It is intended for graduate students and academic and industrial researchers in the field of control, robotics, neural networks, simulation and modelling.
Author: Shuai Li Publisher: John Wiley & Sons ISBN: 1119556988 Category : Technology & Engineering Languages : en Pages : 216
Book Description
Presents pioneering and comprehensive work on engaging movement in robotic arms, with a specific focus on neural networks This book presents and investigates different methods and schemes for the control of robotic arms whilst exploring the field from all angles. On a more specific level, it deals with the dynamic-neural-network based kinematic control of redundant robot arms by using theoretical tools and simulations. Kinematic Control of Redundant Robot Arms Using Neural Networks is divided into three parts: Neural Networks for Serial Robot Arm Control; Neural Networks for Parallel Robot Control; and Neural Networks for Cooperative Control. The book starts by covering zeroing neural networks for control, and follows up with chapters on adaptive dynamic programming neural networks for control; projection neural networks for robot arm control; and neural learning and control co-design for robot arm control. Next, it looks at robust neural controller design for robot arm control and teaches readers how to use neural networks to avoid robot singularity. It then instructs on neural network based Stewart platform control and neural network based learning and control co-design for Stewart platform control. The book finishes with a section on zeroing neural networks for robot arm motion generation. Provides comprehensive understanding on robot arm control aided with neural networks Presents neural network-based control techniques for single robot arms, parallel robot arms (Stewart platforms), and cooperative robot arms Provides a comparison of, and the advantages of, using neural networks for control purposes rather than traditional control based methods Includes simulation and modelling tasks (e.g., MATLAB) for onward application for research and engineering development By focusing on robot arm control aided by neural networks whilst examining central topics surrounding the field, Kinematic Control of Redundant Robot Arms Using Neural Networks is an excellent book for graduate students and academic and industrial researchers studying neural dynamics, neural networks, analog and digital circuits, mechatronics, and mechanical engineering.
Author: Yunong Zhang Publisher: Springer Science & Business Media ISBN: 3642375189 Category : Technology & Engineering Languages : en Pages : 201
Book Description
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: Xuefeng Zhou Publisher: Springer Nature ISBN: 9811555036 Category : Technology & Engineering Languages : en Pages : 138
Book Description
This open access book mainly focuses on the safe control of robot manipulators. The control schemes are mainly developed based on dynamic neural network, which is an important theoretical branch of deep reinforcement learning. In order to enhance the safety performance of robot systems, the control strategies include adaptive tracking control for robots with model uncertainties, compliance control in uncertain environments, obstacle avoidance in dynamic workspace. The idea for this book on solving safe control of robot arms was conceived during the industrial applications and the research discussion in the laboratory. Most of the materials in this book are derived from the authors’ papers published in journals, such as IEEE Transactions on Industrial Electronics, neurocomputing, etc. This book can be used as a reference book for researcher and designer of the robotic systems and AI based controllers, and can also be used as a reference book for senior undergraduate and graduate students in colleges and universities.
Author: Adam Spiers Publisher: Springer ISBN: 3319301608 Category : Technology & Engineering Languages : en Pages : 286
Book Description
This book investigates a biologically inspired method of robot arm control, developed with the objective of synthesising human-like motion dynamically, using nonlinear, robust and adaptive control techniques in practical robot systems. The control method caters to a rising interest in humanoid robots and the need for appropriate control schemes to match these systems. Unlike the classic kinematic schemes used in industrial manipulators, the dynamic approaches proposed here promote human-like motion with better exploitation of the robot’s physical structure. This also benefits human-robot interaction. The control schemes proposed in this book are inspired by a wealth of human-motion literature that indicates the drivers of motion to be dynamic, model-based and optimal. Such considerations lend themselves nicely to achievement via nonlinear control techniques without the necessity for extensive and complex biological models. The operational-space method of robot control forms the basis of many of the techniques investigated in this book. The method includes attractive features such as the decoupling of motion into task and posture components. Various developments are made in each of these elements. Simple cost functions inspired by biomechanical “effort” and “discomfort” generate realistic posture motion. Sliding-mode techniques overcome robustness shortcomings for practical implementation. Arm compliance is achieved via a method of model-free adaptive control that also deals with actuator saturation via anti-windup compensation. A neural-network-centered learning-by-observation scheme generates new task motions, based on motion-capture data recorded from human volunteers. In other parts of the book, motion capture is used to test theories of human movement. All developed controllers are applied to the reaching motion of a humanoid robot arm and are demonstrated to be practically realisable. This book is designed to be of interest to those wishing to achieve dynamics-based human-like robot-arm motion in academic research, advanced study or certain industrial environments. The book provides motivations, extensive reviews, research results and detailed explanations. It is not only suited to practising control engineers, but also applicable for general roboticists who wish to develop control systems expertise in this area.
Author: Yunong Zhang Publisher: John Wiley & Sons ISBN: 1119381428 Category : Technology & Engineering Languages : en Pages : 320
Book Description
Introduces a revolutionary, quadratic-programming based approach to solving long-standing problems in motion planning and control of redundant manipulators This book describes a novel quadratic programming approach to solving redundancy resolutions problems with redundant manipulators. Known as ``QP-unified motion planning and control of redundant manipulators'' theory, it systematically solves difficult optimization problems of inequality-constrained motion planning and control of redundant manipulators that have plagued robotics engineers and systems designers for more than a quarter century. An example of redundancy resolution could involve a robotic limb with six joints, or degrees of freedom (DOFs), with which to position an object. As only five numbers are required to specify the position and orientation of the object, the robot can move with one remaining DOF through practically infinite poses while performing a specified task. In this case redundancy resolution refers to the process of choosing an optimal pose from among that infinite set. A critical issue in robotic systems control, the redundancy resolution problem has been widely studied for decades, and numerous solutions have been proposed. This book investigates various approaches to motion planning and control of redundant robot manipulators and describes the most successful strategy thus far developed for resolving redundancy resolution problems. Provides a fully connected, systematic, methodological, consecutive, and easy approach to solving redundancy resolution problems Describes a new approach to the time-varying Jacobian matrix pseudoinversion, applied to the redundant-manipulator kinematic control Introduces The QP-based unification of robots' redundancy resolution Illustrates the effectiveness of the methods presented using a large number of computer simulation results based on PUMA560, PA10, and planar robot manipulators Provides technical details for all schemes and solvers presented, for readers to adopt and customize them for specific industrial applications Robot Manipulator Redundancy Resolution is must-reading for advanced undergraduates and graduate students of robotics, mechatronics, mechanical engineering, tracking control, neural dynamics/neural networks, numerical algorithms, computation and optimization, simulation and modelling, analog, and digital circuits. It is also a valuable working resource for practicing robotics engineers and systems designers and industrial researchers.
Author: Yongping Pan Publisher: Frontiers Media SA ISBN: 2889661571 Category : Science Languages : en Pages : 132
Book Description
This eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contact.
Author: Shuai Li
Author: Shuai Li
Author: Shuai Li
Author: Shuai Li
Author: Yunong Zhang
Author: Xuefeng Zhou
Author: Adam Spiers
Author: Benjamin B. Choi
Author: Yunong Zhang
Author: Suhjin Horng
Author: Yongping Pan