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. <ul>
<li>Provides comprehensive understanding on robot arm control aided with neural networks</li> <li>Presents neural network-based control techniques for single robot arms, parallel robot arms (Stewart platforms), and cooperative robot arms</li> <li>Provides a comparison of, and the advantages of, using neural networks for control purposes rather than traditional control based methods</li> <li>Includes simulation and modelling tasks (e.g., MATLAB) for onward application for research and engineering development</li> </ul>
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.

Kinematic Control of Redundant Robot Arms Using Neural Networks (IEEE Press)

Computers & Technology

Jin, Long

Li, Shuai

Mirza, Mohammed Aquil

Computer Science

Wiley-IEEE Press

Deep Reinforcement Learning with Guaranteed Performance: A Lyapunov-Based Approach (Studies in Systems, Decision and Control)

Zhou, Xuefeng

Zhang, Yinyan

Reference

Springer

Grasslands on the Third Pole of the World: Structure, Function, Process, and Resilience of Social-Ecological Systems

Politics & Social Sciences

ZHANG YONG

Shen, Hao

Dong, Shikui

Xu, Yudan

Sociology

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.

Neural Networks for Cooperative Control of Multiple Robot Arms (SpringerBriefs in Applied Sciences and Technology)

In this book, we present our systematic investigations into consensus in multi-agent systems. We show the design and analysis of various types of consensus protocols from a multi-agent perspective with a focus on min-consensus and its variants. We also discuss second-order and high-order min-consensus. A very interesting topic regarding the link between consensus and path planning is also included. We show that a biased min-consensus protocol can lead to the path planning phenomenon, which means that the complexity of shortest path planning can emerge from a perturbed version of min-consensus protocol, which as a case study may encourage researchers in the field of distributed control to rethink the nature of complexity and the distance between control and intelligence. We also illustrate the design and analysis of consensus protocols for nonlinear multi-agent systems derived from an optimal control formulation, which do not require solving a Hamilton-Jacobi-Bellman (HJB) equation. The book was written in a self-contained format. For each consensus protocol, the performance is verified through simulative examples and analyzed via mathematical derivations, using tools like graph theory and modern control theory. The book's goal is to provide not only theoretical contributions but also explore underlying intuitions from a methodological perspective.

Machine Behavior Design And Analysis: A Consensus Perspective

<div>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.&nbsp;</div> <div>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.</div>

AI based Robot Safe Learning and Control

Xu, Zhihao

Wu, Hongmin

Cheng, Taobo

Lv, Xiaojing

Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection

This open access book focuses on&nbsp;robot introspection,&nbsp;which&nbsp;has a direct impact on physical human-robot interaction&nbsp;and&nbsp;long-term autonomy,&nbsp;and&nbsp;which can benefit from autonomous anomaly monitoring and diagnosis, as well as anomaly recovery strategies. In robotics,&nbsp;the ability&nbsp;to&nbsp;reason,&nbsp;solve their own&nbsp;anomalies&nbsp;and proactively&nbsp;enrich owned knowledge is a direct way to improve autonomous behaviors. To this end, the authors start by considering the underlying pattern of multimodal observation during robot manipulation, which can&nbsp;effectively&nbsp;be modeled as a parametric&nbsp;hidden Markov&nbsp;model (HMM). They then adopt a nonparametric Bayesian approach in defining a prior using the&nbsp;hierarchical Dirichlet&nbsp;process (HDP) on the standard HMM parameters,&nbsp;known as the&nbsp;Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM). The HDP-HMM can examine an HMM with an unbounded number of possible states and&nbsp;allows flexibility in the complexity of the learned model and the development of reliable and scalable variational inference methods. This book is a&nbsp;valuable&nbsp;reference&nbsp;resource for&nbsp;researchers and designers in&nbsp;the field&nbsp;of robot learning and multimodal perception, as well as for senior undergraduate and graduate&nbsp;university&nbsp;students.

Rojas, Juan

In this book,&nbsp;the authors&nbsp;focus on three aspects&nbsp;related to the development of articulated agents: presenting an overview of high-level control algorithms for intelligent decision-making of articulated agents, experimental study of the properties of soft agents as the end-effector of articulated agents, and accurate management of low-level torque-control loop to accurately control the articulated agents. This book&nbsp;summarizes&nbsp;recent advances related to articulated agents. The motive behind the book is to trigger theoretical and practical research studies related to articulated agents.

Management and Intelligent Decision-Making in Complex Systems: An Optimization-Driven Approach

Science & Math

Khan, Ameer Hamza

Cao, Xinwei

Mathematics

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