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Neural Networks For Robotic Control
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Book Synopsis Neural Network Control Of Robot Manipulators And Non-Linear Systems by : F W Lewis
Download or read book Neural Network Control Of Robot Manipulators And Non-Linear Systems written by F W Lewis and published by CRC Press. This book was released on 2020-08-14 with total page 468 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Neural Networks in Robotics by : George A. Bekey
Download or read book Neural Networks in Robotics written by George A. Bekey and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 560 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural Networks in Robotics is the first book to present an integrated view of both the application of artificial neural networks to robot control and the neuromuscular models from which robots were created. The behavior of biological systems provides both the inspiration and the challenge for robotics. The goal is to build robots which can emulate the ability of living organisms to integrate perceptual inputs smoothly with motor responses, even in the presence of novel stimuli and changes in the environment. The ability of living systems to learn and to adapt provides the standard against which robotic systems are judged. In order to emulate these abilities, a number of investigators have attempted to create robot controllers which are modelled on known processes in the brain and musculo-skeletal system. Several of these models are described in this book. On the other hand, connectionist (artificial neural network) formulations are attractive for the computation of inverse kinematics and dynamics of robots, because they can be trained for this purpose without explicit programming. Some of the computational advantages and problems of this approach are also presented. For any serious student of robotics, Neural Networks in Robotics provides an indispensable reference to the work of major researchers in the field. Similarly, since robotics is an outstanding application area for artificial neural networks, Neural Networks in Robotics is equally important to workers in connectionism and to students for sensormonitor control in living systems.
Book Synopsis Neural Networks for Robotic Control by : Ali M. S. Zalzala
Download or read book Neural Networks for Robotic Control written by Ali M. S. Zalzala and published by Prentice Hall. This book was released on 1996 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Neural Networks for Robotics by : Nancy Arana-Daniel
Download or read book Neural Networks for Robotics written by Nancy Arana-Daniel and published by CRC Press. This book was released on 2018-08-21 with total page 289 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book offers an insight on artificial neural networks for giving a robot a high level of autonomous tasks, such as navigation, cost mapping, object recognition, intelligent control of ground and aerial robots, and clustering, with real-time implementations. The reader will learn various methodologies that can be used to solve each stage on autonomous navigation for robots, from object recognition, clustering of obstacles, cost mapping of environments, path planning, and vision to low level control. These methodologies include real-life scenarios to implement a wide range of artificial neural network architectures.
Book Synopsis Adaptive Neural Network Control of Robotic Manipulators by : Tong Heng Lee
Download or read book Adaptive Neural Network Control of Robotic Manipulators written by Tong Heng Lee and published by World Scientific. This book was released on 1998 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Kinematic Control of Redundant Robot Arms Using Neural Networks by : Shuai Li
Download or read book Kinematic Control of Redundant Robot Arms Using Neural Networks written by Shuai Li and published by John Wiley & Sons. This book was released on 2019-04-29 with total page 214 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Neural Systems for Robotics by : Omid Omidvar
Download or read book Neural Systems for Robotics written by Omid Omidvar and published by Elsevier. This book was released on 2012-12-02 with total page 369 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural Systems for Robotics represents the most up-to-date developments in the rapidly growing aplication area of neural networks, which is one of the hottest application areas for neural networks technology. The book not only contains a comprehensive study of neurocontrollers in complex Robotics systems, written by highly respected researchers in the field but outlines a novel approach to solving Robotics problems. The importance of neural networks in all aspects of Robot arm manipulators, neurocontrol, and Robotic systems is also given thorough and in-depth coverage. All researchers and students dealing with Robotics will find Neural Systems for Robotics of immense interest and assistance. Focuses on the use of neural networks in robotics-one of the hottest application areas for neural networks technology Represents the most up-to-date developments in this rapidly growing application area of neural networks Contains a new and novel approach to solving Robotics problems
Book Synopsis Artificial Intelligence in Industrial Decision Making, Control and Automation by : S.G. Tzafestas
Download or read book Artificial Intelligence in Industrial Decision Making, Control and Automation written by S.G. Tzafestas and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 778 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is concerned with Artificial Intelligence (AI) concepts and techniques as applied to industrial decision making, control and automation problems. The field of AI has been expanded enormously during the last years due to that solid theoretical and application results have accumulated. During the first stage of AI development most workers in the field were content with illustrations showing ideas at work on simple problems. Later, as the field matured, emphasis was turned to demonstrations that showed the capability of AI techniques to handle problems of practical value. Now, we arrived at the stage where researchers and practitioners are actually building AI systems that face real-world and industrial problems. This volume provides a set of twenty four well-selected contributions that deal with the application of AI to such real-life and industrial problems. These contributions are grouped and presented in five parts as follows: Part 1: General Issues Part 2: Intelligent Systems Part 3: Neural Networks in Modelling, Control and Scheduling Part 4: System Diagnostics Part 5: Industrial Robotic, Manufacturing and Organizational Systems Part 1 involves four chapters providing background material and dealing with general issues such as the conceptual integration of qualitative and quantitative models, the treatment of timing problems at system integration, and the investigation of correct reasoning in interactive man-robot systems.
Book Synopsis Adaptive Neural Network Control of Robotic Manipulators by :
Download or read book Adaptive Neural Network Control of Robotic Manipulators written by and published by . This book was released on with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis High-Level Feedback Control with Neural Networks by : Young Ho Kim
Download or read book High-Level Feedback Control with Neural Networks written by Young Ho Kim and published by World Scientific. This book was released on 1998 with total page 232 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 areintuitive, 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 :Shuzhi S. Ge Publisher :World Scientific Series In Robotics And Intelligent Systems ISBN 13 :9789810234522 Total Pages :381 pages Book Rating :4.2/5 (345 download)
Book Synopsis Adaptive Neural Network Control of Robotic Manipulators by : Shuzhi S. Ge
Download or read book Adaptive Neural Network Control of Robotic Manipulators written by Shuzhi S. Ge and published by World Scientific Series In Robotics And Intelligent Systems. This book was released on 1998 with total page 381 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Deep Learning for Robot Perception and Cognition by : Alexandros Iosifidis
Download or read book Deep Learning for Robot Perception and Cognition written by Alexandros Iosifidis and published by Academic Press. This book was released on 2022-02-04 with total page 638 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning for Robot Perception and Cognition introduces a broad range of topics and methods in deep learning for robot perception and cognition together with end-to-end methodologies. The book provides the conceptual and mathematical background needed for approaching a large number of robot perception and cognition tasks from an end-to-end learning point-of-view. The book is suitable for students, university and industry researchers and practitioners in Robotic Vision, Intelligent Control, Mechatronics, Deep Learning, Robotic Perception and Cognition tasks. Presents deep learning principles and methodologies Explains the principles of applying end-to-end learning in robotics applications Presents how to design and train deep learning models Shows how to apply deep learning in robot vision tasks such as object recognition, image classification, video analysis, and more Uses robotic simulation environments for training deep learning models Applies deep learning methods for different tasks ranging from planning and navigation to biosignal analysis
Book Synopsis Neural Networks for Cooperative Control of Multiple Robot Arms by : Shuai Li
Download or read book Neural Networks for Cooperative Control of Multiple Robot Arms written by Shuai Li and published by Springer. This book was released on 2017-10-29 with total page 74 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis AI based Robot Safe Learning and Control by : Xuefeng Zhou
Download or read book AI based Robot Safe Learning and Control written by Xuefeng Zhou and published by Springer Nature. This book was released on 2020-06-02 with total page 138 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Kinematic Control of Redundant Robot Arms Using Neural Networks by : Shuai Li
Download or read book Kinematic Control of Redundant Robot Arms Using Neural Networks written by Shuai Li and published by John Wiley & Sons. This book was released on 2019-02-11 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Competition-Based Neural Networks with Robotic Applications by : Shuai Li
Download or read book Competition-Based Neural Networks with Robotic Applications written by Shuai Li and published by Springer. This book was released on 2017-05-30 with total page 121 pages. Available in PDF, EPUB and Kindle. Book excerpt: Focused on solving competition-based problems, this book designs, proposes, develops, analyzes and simulates various neural network models depicted in centralized and distributed manners. Specifically, it defines four different classes of centralized models for investigating the resultant competition in a group of multiple agents. With regard to distributed competition with limited communication among agents, the book presents the first distributed WTA (Winners Take All) protocol, which it subsequently extends to the distributed coordination control of multiple robots. Illustrations, tables, and various simulative examples, as well as a healthy mix of plain and professional language, are used to explain the concepts and complex principles involved. Thus, the book provides readers in neurocomputing and robotics with a deeper understanding of the neural network approach to competition-based problem-solving, offers them an accessible introduction to modeling technology and the distributed coordination control of redundant robots, and equips them to use these technologies and approaches to solve concrete scientific and engineering problems.
Book Synopsis Intelligent Control of Robotic Systems by : D. Katic
Download or read book Intelligent Control of Robotic Systems written by D. Katic and published by Springer Science & Business Media. This book was released on 2013-03-14 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: As robotic systems make their way into standard practice, they have opened the door to a wide spectrum of complex applications. Such applications usually demand that the robots be highly intelligent. Future robots are likely to have greater sensory capabilities, more intelligence, higher levels of manual dexter ity, and adequate mobility, compared to humans. In order to ensure high-quality control and performance in robotics, new intelligent control techniques must be developed, which are capable of coping with task complexity, multi-objective decision making, large volumes of perception data and substantial amounts of heuristic information. Hence, the pursuit of intelligent autonomous robotic systems has been a topic of much fascinating research in recent years. On the other hand, as emerging technologies, Soft Computing paradigms consisting of complementary elements of Fuzzy Logic, Neural Computing and Evolutionary Computation are viewed as the most promising methods towards intelligent robotic systems. Due to their strong learning and cognitive ability and good tolerance of uncertainty and imprecision, Soft Computing techniques have found wide application in the area of intelligent control of robotic systems.
