Management and Intelligent Decision-Making in Complex Systems: An Optimization-Driven Approach

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In this book, the authors focus on three aspects related to the development of articulated agents: presenting an overview of high-level control algorithms for intelligent decision-making of articulated agents, experimental study of the properties of soft agents as the end-effector of articulated agents, and accurate management of low-level torque-control loop to accurately control the articulated agents. This book summarizes recent advances related to articulated agents. The motive behind the book is to trigger theoretical and practical research studies related to articulated agents.

Summarizes the recent progresses of the management of articulated agents, intelligent decision-making algorithm, and smart mechanical designs Provides comprehensive theoretical and experimental results along with detailed discussion on different factors affecting the performance Explains simulation and fabrication of experimental platforms in detail to make the results easily replicable for interested readers

Autorentext

Ameer Hamza Khan received the B.S. degree in electrical engineering from the Pakistan Institute of Engineering and Applied Sciences, Islamabad, Pakistan, in 2015. He is currently working toward the Ph.D. degree with the Department of Computing, The Hong Kong Polytechnic University, Hong Kong. He was a Research Assistant with the Department of Computing, The Hong Kong Polytechnic University. His research interests include nonlinear optimization, metaheuristic algorithms, adaptive control, and machine learning.
Dr. Xinwei Cao received the bachelor degree from Shandong University China, the Master degree from Tongji University China, Hefei, China, and the Ph.D. from Fudan University China all in management. She is currently a lecturer at Shanghai University China. Her research interests includes data analytics for management and accounting, engineering and firm management.
Shuai Li received the B.E. degree in precision mechanical engineering from the Hefei University of Technology, Hefei, China, in 2005, the M.E. degree in automatic control engineering from the University of Science and Technology of China, Hefei, China, in 2008, and the Ph.D. degree in electrical and computer engineering from the Stevens Institute of Technology, Hoboken, NJ, USA, in 2014. He is currently a professor at Lanzhou University China and the research reported in this work is partially supported by Lanzhou University. His current research interests include neural networks and meta-optimization for management.



Klappentext

In this book, the authors focus on three aspects related to the development of articulated agents: presenting an overview of high-level control algorithms for intelligent decision-making of articulated agents, experimental study of the properties of soft agents as the end-effector of articulated agents, and accurate management of low-level torque-control loop to accurately control the articulated agents. This book summarizes recent advances related to articulated agents. The motive behind the book is to trigger theoretical and practical research studies related to articulated agents.


Inhalt
Obstacle Avoidance Based Decision Making and Management of Articulated Agents.- Management of Soft Agents with Structural Uncertainty.- A Novel Damping Mechanism for Soft Agents with Structural Uncertainty.- Management of Electrical Machine Using Torque Control Strategy.

Weitere Informationen

  • Allgemeine Informationen
    • GTIN 09789811593918
    • Genre Elektrotechnik
    • Auflage 1st edition 2021
    • Sprache Englisch
    • Lesemotiv Verstehen
    • Anzahl Seiten 96
    • Größe H235mm x B155mm x T6mm
    • Jahr 2020
    • EAN 9789811593918
    • Format Kartonierter Einband
    • ISBN 9811593914
    • Veröffentlichung 30.10.2020
    • Titel Management and Intelligent Decision-Making in Complex Systems: An Optimization-Driven Approach
    • Autor Ameer Hamza Khan , Shuai Li , Xinwei Cao
    • Gewicht 160g
    • Herausgeber Springer Nature Singapore

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