Learning Control : Applications in Robotics and Complex Dynamical Systems

Dan Zhang

ISBN 10: 0128223146 / ISBN 13: 9780128223147

Published by Elsevier Science & Technology, Elsevier, 2020

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - Learning Control: Applications in Robotics and Complex Dynamical Systems provides a foundational understanding of control theory while also introducing exciting cutting-edge technologies in the field of learning-based control. State-of-the-art techniques involving machine learning and artificial intelligence (AI) are covered, as are foundational control theories and more established techniques such as adaptive learning control, reinforcement learning control, impedance control, and deep reinforcement control. Each chapter includes case studies and real-world applications in robotics, AI, aircraft and other vehicles and complex dynamical systems. Computational methods for control systems, particularly those used for developing AI and other machine learning techniques, are also discussed at length.

Seller Inventory # 9780128223147

Bibliographic Details

• Title: Learning Control : Applications in Robotics and Complex Dynamical Systems
• Publisher: Elsevier Science & Technology, Elsevier
• Publication Date: 2020
• Language: English
• ISBN 10: 0128223146
• ISBN 13: 9780128223147
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 229W x 152H x 13D millimeters 229 millimeters width by 152 millimeters height by 13 millimeters depth
• Weight: 460 grams

About this title

Synopsis

Learning Control: Applications in Robotics and Complex Dynamical Systems provides a foundational understanding of control theory while also introducing exciting cutting-edge technologies in the field of learning-based control. State-of-the-art techniques involving machine learning and artificial intelligence (AI) are covered, as are foundational control theories and more established techniques such as adaptive learning control, reinforcement learning control, impedance control, and deep reinforcement control. Each chapter includes case studies and real-world applications in robotics, AI, aircraft and other vehicles and complex dynamical systems. Computational methods for control systems, particularly those used for developing AI and other machine learning techniques, are also discussed at length.

• Provides foundational control theory concepts, along with advanced techniques and the latest advances in adaptive control and robotics
• Introduces state-of-the-art learning-based control technologies and their applications in robotics and other complex dynamical systems
• Demonstrates computational techniques for control systems
• Covers iterative learning impedance control in both human-robot interaction and collaborative robots

About the Authors

Dan Zhang is a Kaneff Professor and Tier 1 York Research Chair in Advanced Robotics and Mechatronics in the Department of Mechanical Engineering of the Lassonde School of Engineering at York University, Toronoto, Canada. Previously he was Professor and Canada Research Chair in Advanced Robotics and Automation, and he was a founding Chair of the Department of Automotive, Mechanical, and Manufacturing Engineering with the Faculty of Engineering and Applied Science at University of Ontario Institute of Technology. He is editor-in-chief for International Journal of Robotics Applications and Technologies, Associate editor for the International Journal of Robotics and Automation (ACTA publisher), and guest editor on four other international journals. He is the editor of 6 books related to mechatronics and robotics.

Bin Wei is an Assistant Professor at Algoma University, Ontario, Canada. He received his Ph.D. in robotics from University of Ontario Institute of Technology, Canada, in 2016. He conducts research in the areas of robotics, control theory, and computational mechanics. He has co-edited 5 books on robotic mechanics.