Data-driven Strategies for Soft Sensing, Robot Modelling and Control PDF Download
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Author: 王奎
Publisher:
ISBN:
Category : Robotics
Languages : en
Pages : 198
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Book Description
Author: 王奎
Publisher:
ISBN:
Category : Robotics
Languages : en
Pages : 198
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Book Description
Author: Cong Wang
Publisher:
ISBN:
Category :
Languages : en
Pages : 89
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Book Description
In a sensing rich system, a large amount of data can be obtained over time and utilized to improve the performance and functionality of a robotic system. Data-driven approaches emphasize on the utilization of auxiliary sensors, sensor fusion, and data learning. Real-time control systems of robotic systems often run at kilo-Hertz sampling frequencies. New data is obtained from a variety of feedback sources every one or a few milliseconds. Auxiliary sensors provide additional feedbacks and enable sensor fusion. This dissertation presents a series of data-driven approaches to improve the sensing and control of robot manipulators from several aspects, including sensor fusion for motion sensing, statistical learning for feedback compensation, nonparametric learning control, and intelligent modeling and identification. In regard to the limited sensing capability of conventional indirect drive-trains of industrial robots, a sensor fusion approach based on auxiliary optical and inertial sensors is introduced for direct motion sensing of robot end-effectors. The approach is especially useful to applications where high accuracy is required for end-effector performance in real-time. Meanwhile, for the scenarios where auxiliary sensor are not allowed, a statistical learning algorithms is developed for sensing compensation so that control of systems with limited feedback capability can be significantly improved. A major application of the approach is vision guidance of industrial robots. The proposed learning approach can significantly increase the visual tracking bandwidth without requiring high-speed cameras. Besides improving the sensing capability of robots, nonparametric learning control is developed to control systems with complex dynamics. A major motivation of the approach is robotic laser and plasma cutting. Furthermore, to obtain high-fidelity models more efficiently, planning and learning algorithms are discussed for intelligent system modeling and identification. The applications of the proposed approaches range from vision guided robotic material handling to precision robotic machining. Various tests are designed to validate the proposed approaches.
Author: Esteban Fernando Lopez
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Author: Ömer Melih Gül
Publisher: Springer Nature
ISBN: 3031644956
Category :
Languages : en
Pages : 205
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Book Description
Author: Long Jin
Publisher: Frontiers Media SA
ISBN: 2832552013
Category : Science
Languages : en
Pages : 301
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Book Description
Neural network control has been a research hotspot in academic fields due to the strong ability of computation. One of its wildly applied fields is robotics. In recent years, plenty of researchers have devised different types of dynamic neural network (DNN) to address complex control issues in robotics fields in reality. Redundant manipulators are no doubt indispensable devices in industrial production. There are various works on the redundancy resolution of redundant manipulators in performing a given task with the manipulator model information known. However, it becomes knotty for researchers to precisely control redundant manipulators with unknown model to complete a cyclic-motion generation CMG task, to some extent. It is worthwhile to investigate the data-driven scheme and the corresponding novel dynamic neural network (DNN), which exploits learning and control simultaneously. Therefore, it is of great significance to further research the special control features and solve challenging issues to improve control performance from several perspectives, such as accuracy, robustness, and solving speed.
Author: Didier Theilliol
Publisher: Springer Nature
ISBN: 3031275403
Category : Technology & Engineering
Languages : en
Pages : 352
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Book Description
The book consists of recent works on several axes either with a more theoretical nature or with a focus on applications, which will span a variety of up-to-date topics in the field of systems and control. The main market area of the contributions include: Advanced fault-tolerant control, control reconfiguration, health monitoring techniques for industrial systems, data-driven diagnosis methods, process supervision, diagnosis and control of discrete-event systems, maintenance and repair strategies, statistical methods for fault diagnosis, reliability and safety of industrial systems artificial intelligence methods for control and diagnosis, health-aware control design strategies, advanced control approaches, deep learning-based methods for control and diagnosis, reinforcement learning-based approaches for advanced control, diagnosis and prognosis techniques applied to industrial problems, Industry 4.0 as well as instrumentation and sensors. These works constitute advances in the aforementioned scientific fields and will be used by graduate as well as doctoral students along with established researchers to update themselves with the state of the art and recent advances in their respective fields. As the book includes several applicative studies with several multi-disciplinary contributions (deep learning, reinforcement learning, model-based/data-based control etc.), the book proves to be equally useful for the practitioners as well industrial professionals.
Author: Cybellium Ltd
Publisher: Cybellium Ltd
ISBN:
Category : Computers
Languages : en
Pages : 302
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Book Description
Embark on an Enlightening Journey to "Mastering Robot Dynamics" In a world driven by automation and robotics, mastering the intricacies of robot dynamics is pivotal for creating advanced robotic systems that move with precision and intelligence. "Mastering Robot Dynamics" is your ultimate guide to navigating the complex world of robot motion, control, and manipulation. Whether you're an engineer, researcher, robotics enthusiast, or student, this book equips you with the knowledge and skills needed to excel in designing and controlling sophisticated robotic mechanisms. About the Book: "Mastering Robot Dynamics" takes you on a transformative journey through the intricacies of robot motion and control, from foundational concepts to advanced techniques. From kinematics and dynamics to trajectory planning and real-time control, this book covers it all. Each chapter is meticulously designed to provide both a deep understanding of the principles and practical applications in real-world robotic scenarios. Key Features: · Foundational Understanding: Build a solid foundation by comprehending the core principles of robot dynamics, including kinematics, inertia, and motion equations. · Robot Kinematics: Explore forward and inverse kinematics, understanding how robots move and calculating joint configurations. · Robot Dynamics: Dive into the study of forces, torques, and motion equations, learning how robots interact with their environments. · Trajectory Planning: Master the art of planning robot paths and trajectories, considering constraints and optimizing motion sequences. · Sensors and Perception: Gain insights into sensor integration, perception systems, and how robots interact with the world through feedback. · Motion Control: Learn about different types of control strategies, from PID control to advanced techniques like model predictive control. · Collision Avoidance: Understand methods for detecting and avoiding collisions, ensuring safety and reliability in robot operations. · Robot Manipulation: Explore techniques for manipulating objects, including grasp planning, manipulation tasks, and robotic arms. · Challenges and Trends: Discover challenges in robot dynamics, from sensor noise to complex control algorithms, and explore emerging trends shaping the future of robotics. Who This Book Is For: "Mastering Robot Dynamics" is designed for engineers, researchers, robotics enthusiasts, students, and anyone passionate about robotics. Whether you're aiming to enhance your skills or embark on a journey toward becoming a robotics expert, this book provides the insights and tools to navigate the complexities of designing and controlling robotic systems. © 2023 Cybellium Ltd. All rights reserved. www.cybellium.com
Author: Juntian Qu
Publisher: Bentham Science Publishers
ISBN: 9815256483
Category : Computers
Languages : en
Pages : 285
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Book Description
Introduction to Advanced Soft Robotics is an introductory textbook on soft body robotics. The content is designed to enable readers to better understand soft body robotics. Starting with an introduction to the subject, contents explain fundamental concepts such as perception and sensing, fabrication techniques and material design. These introductory chapters demonstrate the design concept and related design structures of soft robots from multiple perspectives, which can provide considerable design references for robotics learners and enthusiasts. Next, the book explains modeling and control for soft robotics and the applications. Key features of this book include easy-to-understand language and format, simple illustrations and a balanced overview of the subject (including a section on challenges and future prospects for soft robotics), and scientific references.
Author: Shekhar Sharma
Publisher:
ISBN:
Category : Just-in-time systems
Languages : en
Pages : 104
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Book Description
A number of industrial processes involve variables that cannot be reliably measured in real time using online sensors. Many such variables are required as inputs in control schemes to ensure safe and efficient plant operation. Laboratory analysis, which is a reliable method of measuring these variables, is slow and infrequent. Thus, mathematical models called soft sensors which can estimate these hard to measure variables from the abundantly available online process measurements have been used in a number of industrial applications. Among the various soft sensor applications of online prediction, process monitoring, fault detection and isolation, the focus of this thesis is on online prediction and parameter estimation applications. Just-In-Time (JIT) modeling is a unique framework wherein a local model is created every time a prediction is required. One of the most critical components of JIT models is the similarity criterion which determines the data used in the local models and their associated weights. To handle nonlinear and time varying systems simultaneously under the JIT framework, a new similarity metric which incorporates time, along with the traditional space distance, to evaluate sample weights, is proposed. Further, a query based method to determine the bandwidth of the local models adaptively, as an alternative to the offline global method, is also developed. Next, the distance-angle similarity criterion used in modeling dynamic systems under the JIT technique is studied. An improved weighing scheme is then proposed which enables a more accurate selection of data for local modeling and provides a better interpretation of results. Again, for this proposed weighing scheme also, an alternative to the global bandwidth estimation, called the point-based method, is proposed. In the field of online soft sensor prediction and parameter estimation applications, adaptive linear regression algorithms such as recursive least squares and moving window least squares are widely used because of their simplicity and ease of implementation. However, these methods are not robust to outlying values. We develop a new robust and adaptive algorithm with a cautious parameter update strategy. The proposed algorithm is also quite flexible and a number of variants are easily formulated. Finally, advantages of the methods are clearly illustrated by applications to numerical examples, experimental data and industrial case studies.
Author: Concepción A. Monje
Publisher: Frontiers Media SA
ISBN: 2889710475
Category : Technology & Engineering
Languages : en
Pages : 175
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Book Description
