Learning Control

Learning Control

Applications in Robotics and Complex Dynamical Systems

1st Edition - December 5, 2020

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  • Editors: Dan Zhang, Bin Wei
  • Paperback ISBN: 9780128223147
  • eBook ISBN: 9780128223154

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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.

Key Features

  • 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

Readership

Researchers and academics in robotics, mechanical engineering, and mechatronics; engineers working in the same fields; grad students

Table of Contents

    1. A high-level design process for neural-network controls through a framework of human personalities
    2. Cognitive load estimation for adaptive human–machine system automation
    3. Comprehensive error analysis beyond system innovations in Kalman filtering
    4. Nonlinear control
    5. Deep learning approaches in face analysis
    6. Finite multi-dimensional generalized Gamma Mixture Model Learning for feature selection
    7. Variational learning of finite shifted scaled Dirichlet mixture models
    8. From traditional to deep learning: Fault diagnosis for autonomous vehicles
    9. Controlling satellites with reaction wheels
    10. Vision dynamics-based learning control

Product details

  • No. of pages: 280
  • Language: English
  • Copyright: © Elsevier 2020
  • Published: December 5, 2020
  • Imprint: Elsevier
  • Paperback ISBN: 9780128223147
  • eBook ISBN: 9780128223154

About the Editors

Dan Zhang

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.

Affiliations and Expertise

Kaneff Professor and Tier 1 York Research Chair in Advanced Robotics and Mechatronics, Department of Mechanical Engineering of the Lassonde School of Engineering, York University, Toronto, Canada

Bin Wei

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.

Affiliations and Expertise

Assistant Professor, Algoma University, Ontario, Canada

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