Iterative Adaptive Dynamic Programming For Self-learning Optimal Control (Hardcover)

Qinglai Wei

ISBN 10: 9819811201 / ISBN 13: 9789819811205

Published by World Scientific Publishing Co Pte Ltd, Singapore, 2025

About this Item

Hardcover. This unique book introduces the iterative adaptive dynamic programming theory from the control systems perspectives, with the most recent results of iterative adaptive dynamic programming methods. Advanced theoretical analysis and some practical applications of iterative adaptive dynamic programming are provided. Furthermore, the practical applications in residential energy systems are also highlighted, showing the good performance of the iterative adaptive dynamic programming methods.The useful reference text benefits professionals, researchers, academics, graduate and undergraduates students in control engineering. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Seller Inventory # 9789819811205

Bibliographic Details

• Title: Iterative Adaptive Dynamic Programming For Self-learning Optimal Control (Hardcover)
• Publisher: World Scientific Publishing Co Pte Ltd, Singapore
• Publication Date: 2025
• Language: English
• ISBN 10: 9819811201
• ISBN 13: 9789819811205
• Binding: Hardcover
• Condition: new

About this title

Synopsis

This unique book introduces the iterative adaptive dynamic programming theory from the control systems perspectives, with the most recent results of iterative adaptive dynamic programming methods. Advanced theoretical analysis and some practical applications of iterative adaptive dynamic programming are provided. Furthermore, the practical applications in residential energy systems are also highlighted, showing the good performance of the iterative adaptive dynamic programming methods.

The useful reference text benefits professionals, researchers, academics, graduate and undergraduates students in control engineering.

About the Authors

Qinglai Wei received the BS degree in automation and the PhD degree in control theory and control engineering from Northeastern University, Shenyang, China, in 2002 and 2009, respectively. From 2009 to 2011, he was a Post-Doctoral Fellow with the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China. He is currently a Professor at the Institute and an Associate Director at the Laboratory. He has authored four books, and published over 80 international journal articles. His research interests include adaptive dynamic programming, neural networks-based control, optimal control, nonlinear systems, and their industrial applications.

Ruizhuo Song received the PhD degree in control theory and control engineering from Northeastern University, Shenyang, China, in 2012. She was a Post-Doctoral Fellow with the University of Science and Technology Beijing, Beijing, China. She was a Visiting Scholar with the Department of Electrical Engineering, The University of Texas at Arlington, Arlington, TX, USA, from 2013 to 2014. She was a Visiting Scholar with the Department of Electrical, Computer, and Biomedical Engineering, The University of Rhode Island, Kingston, RI, USA, from January 2018 to February 2018. She is currently a Professor with the School of Automation and Electrical Engineering, University of Science and Technology Beijing. She has published over 70 journal articles and conference papers. She has coauthored three monographs. Her current research interests include optimal control, multiplayer games, intelligent computation, intelligent pancreas, and intelligent economy.

Hongyang Li received the PhD degree in computer application technology from the University of Chinese Academy of Sciences, Beijing, China, in 2022. He is currently working as an Assistant Professor with the State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China. His research interests include adaptive dynamic programming, reinforcement learning, optimal control, and their industrial applications.