基于强化学习人机交互控制 Human-Robot Interaction Control Using Reinforcement Learning

基本信息

Series:IEEE Press Series on Systems Science and Engineering

Format:Hardback 288 pages

Publisher:John Wiley & Sons Inc

Imprint:Wiley-IEEE Press

ISBN:9781119782742

Published:5 Nov 2021

Weight:454g

页面参数仅供参考，具体以实物为准

书籍简介

全面探索人机交互的控制方案

在《使用强化学习人机交互控制》中，专家团队简要概述了人机交互控制方案，并深入介绍了新颖的无模型和强化学习控制器。本书先简要介绍了先进的人机交互控制和强化学习，然后介绍了典型的环境模型。作者还描述了一些知名的参数估计识别技术。

《使用强化学习人机交互控制》提供了严格的数学处理和演示，有助于理解控制方案和算法。它还描述了人机交互控制和基于强化学习控制的稳定性和收敛性分析。

作者还讨论了先进和前沿的主题，如逆运动学和速度运动学解决方案、H2 神经控制以及机器人领域可能出现的发展。

读者还将享受：

基于模型的人机交互控制的全面介绍

使用欧拉角的无模型人机交互控制和人在环控制的全面探索

强化学习在机器人位置和力控制以及连续时间强化学习在机器人力控制中的实际讨论

使用强化学习深入研究坏情况下的不确定性机器人控制以及使用多智能体强化学习控制冗余机器人

《使用强化学习进行人机交互控制》非常适合在机器人、学习控制系统、神经网络和计算智能领域学习和工作的高年级本科生和研究生、学术研究人员和工业从业者，也是学习强化学习学生和专业人士不可或缺的资源。

A comprehensive exploration of the control schemes of human-robot interactions 

In Human-Robot Interaction Control Using Reinforcement Learning, an expert team of authors delivers a concise overview of human-robot interaction control schemes and insightful presentations of novel, model-free and reinforcement learning controllers. The book begins with a brief introduction to state-of-the-art human-robot interaction control and reinforcement learning before moving on to describe the typical environment model. The authors also describe some of the most famous identification techniques for parameter estimation. 

Human-Robot Interaction Control Using Reinforcement Learning offers rigorous mathematical treatments and demonstrations that facilitate the understanding of control schemes and algorithms. It also describes stability and convergence analysis of human-robot interaction control and reinforcement learning based control. 

The authors also discuss advanced and cutting-edge topics, like inverse and velocity kinematics solutions, H2 neural control, and likely upcoming developments in the field of robotics. 

Readers will also enjoy:  

A thorough introduction to model-based human-robot interaction control 

Comprehensive explorations of model-free human-robot interaction control and human-in-the-loop control using Euler angles 

Practical discussions of reinforcement learning for robot position and force control, as well as continuous time reinforcement learning for robot force control 

In-depth examinations of robot control in worst-case uncertainty using reinforcement learning and the control of redundant robots using multi-agent reinforcement learning  

Perfect for senior undergraduate and graduate students, academic researchers, and industrial practitioners studying and working in the fields of robotics, learning control systems, neural networks, and computational intelligence, Human-Robot Interaction Control Using Reinforcement Learning is also an indispensable resource for students and professionals studying reinforcement learning. 

作者简介

WEN YU，博士，墨西哥墨西哥城理工学院（CINVESTAV-IPN）研究与高级研究中心自动控制系教授兼系主任。他是《使用模糊方程和 Z 数对不确定非线性系统进行建模和控制》一书的合著者。

ADOLFO PERRUSQUÍA 博士是英国贝德福德克兰菲尔德大学航空航天、运输和制造学院的研究员。

WEN YU, PhD, is Professor and Head of the Departamento de Control Automático with the Centro de Investigación y de Estudios Avanzados, Instituto Politécnico Nacional (CINVESTAV-IPN), Mexico City, Mexico. He is a co-author of Modeling and Control of Uncertain Nonlinear Systems with Fuzzy Equations and Z-Number.

ADOLFO PERRUSQUÍA, PhD, is a Research Fellow in the School of Aerospace, Transport, and Manufacturing at Cranfield University in Bedford, UK.

部分目录，仅供参考

Author Biographies xi

List of Figures xiii

List of Tables xvii

Preface xix

Part I Human-robot Interaction Control 1

1 Introduction 3

1.1 Human-Robot Interaction Control 3

1.2 Reinforcement Learning for Control 6

1.3 Structure of the Book 7

References 10

2 Environment Model of Human-Robot Interaction 17

2.1 Impedance and Admittance 17

2.2 Impedance Model for Human-Robot Interaction 21

2.3 Identification of Human-Robot Interaction Model 24

2.4 Conclusions 30

References 30

3 Model Based Human-Robot Interaction Control 33

3.1 Task Space Impedance/Admittance Control 33

3.2 Joint Space Impedance Control 36

3.3 Accuracy and Robustness 37

3.4 Simulations 39

3.5 Conclusions 42

References 44

4 Model Free Human-Robot Interaction Control 45

4.1 Task-Space Control Using Joint-Space Dynamics 45

4.2 Task-Space Control Using Task-Space Dynamics 52

4.3 Joint Space Control 53

4.4 Simulations 54

4.5 Experiments 55

4.6 Conclusions 68

References 71

5 Human-in-the-loop Control Using Euler Angles 73

5.1 Introduction 73

5.2 Joint-Space Control 74

5.3 Task-Space Control 79

5.4 Experiments 83

5.5 Conclusions 92