Learning Control
Applications in Robotics and Complex Dynamical Systems
Published on 10. December 2020
Book
Paperback/Softback
280 pages
978-0-12-822314-7 (ISBN)
Description
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.
More details
Language
English
Place of publication
United States
Target group
Professional and scholarly
Illustrations
Approx. 110 illustrations (30 in full color)
Dimensions
Height: 229 mm
Width: 152 mm
Weight
520 gr
ISBN-13
978-0-12-822314-7 (9780128223147)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
E-Book
12/2020
Elsevier
€175.00
Available for download
Persons
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.
Content
A high-level design process for neural-network controls through a framework of human personalities
Cognitive load estimation for adaptive human-machine system automation
Comprehensive error analysis beyond system innovations in Kalman filtering
Nonlinear control
Deep learning approaches in face analysis
Finite multi-dimensional generalized Gamma Mixture Model Learning for feature selection
Variational learning of finite shifted scaled Dirichlet mixture models
From traditional to deep learning: Fault diagnosis for autonomous vehicles
Controlling satellites with reaction wheels
Vision dynamics-based learning control
Cognitive load estimation for adaptive human-machine system automation
Comprehensive error analysis beyond system innovations in Kalman filtering
Nonlinear control
Deep learning approaches in face analysis
Finite multi-dimensional generalized Gamma Mixture Model Learning for feature selection
Variational learning of finite shifted scaled Dirichlet mixture models
From traditional to deep learning: Fault diagnosis for autonomous vehicles
Controlling satellites with reaction wheels
Vision dynamics-based learning control