Physics-Generated AIs of Robust Nonlinear Filter and Control Designs for Complicated Man-Made Machines (Hardcover)
Language: English
Published by Taylor & Francis Ltd, London, 2026
- Hardcover
- New
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Hardcover. This book introduces a robust H physics-generated AI-driven filter and controller, along with a nonlinear Luenberger observer model and a state estimation error dynamic model, to effectively address HJIEs for robust H state estimation (filtering) and reference trajectory tracking control in nonlinear stochastic systems. Additionally, it presents a method for training deep neural networks (DNNs) using these models, alongside a physics-generated AI-driven observer-based reference tracking control scheme, with applications in the guidance and control of relevant systems.Key features:Provides theoretical analysis and detailed design procedure for physics-generated AI-driven H or mixed H2/H filterApplies physics-generated AI-driven robust H or mixed H2/H filter and reference tracking control schemes to the trajectory estimation and reference tracking control of man-made machinesIntroduces physics-generated AI-driven decentralized H observer-based team formation tracking control of large-scale quadrotor UAVs, biped robots or LEO satellitesPromulgates the idea of the forthcoming age of physics-generated AI in robotDescribes robust physics-generated AI-driven filter and control schemes for complex man-made machinesThis book is aimed at graduate students and researchers in control science, signal processing and artificial intelligence. This book introduces a robust H physical generative AI-driven filter and controller, along with a nonlinear Luenberger observer model and a state estimation error dynamic model, to effectively address HJIEs for robust H state estimation (filtering) and reference trajectory tracking control in nonlinear stochastic systems. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…
Seller Inventory # 9781041129349
- Title
- Physics-Generated AIs of Robust Nonlinear Filter and Control Designs for Complicated Man-Made Machines (Hardcover)
- Author
- Bor-Sen Chen
- Publisher
- Taylor & Francis Ltd, London
- Publication year
- 2026
- Condition
- new
- Binding
- Hardcover
- Language
- English
- ISBN 10
- 1041129343
- ISBN 13
- 9781041129349
This book introduces a robust H∞ physics-generated AI-driven filter and controller, along with a nonlinear Luenberger observer model and a state estimation error dynamic model, to effectively address HJIEs for robust H∞ state estimation (filtering) and reference trajectory tracking control in nonlinear stochastic systems. Additionally, it presents a method for training deep neural networks (DNNs) using these models, alongside a physics-generated AI-driven observer-based reference tracking control scheme, with applications in the guidance and control of relevant systems.
Key features:
- Provides theoretical analysis and detailed design procedure for physics-generated AI-driven H∞ or mixed H2/H∞ filter
- Applies physics-generated AI-driven robust H∞ or mixed H2/H∞ filter and reference tracking control schemes to the trajectory estimation and reference tracking control of man-made machines
- Introduces physics-generated AI-driven decentralized H∞ observer-based team formation tracking control of large-scale quadrotor UAVs, biped robots or LEO satellites
- Promulgates the idea of the forthcoming age of physics-generated AI in robot
- Describes robust physics-generated AI-driven filter and control schemes for complex man-made machines
This book is aimed at graduate students and researchers in control science, signal processing and artificial intelligence.
"Synopsis" may belong to another edition of this title.
About the Author
Bor-Sen Chen received his BS in electrical engineering from Tatung Institute of Technology, Taipei, Taiwan, in 1970, and MS in geophysics from National Central University, Chungli, Taiwan, in 1973, and PhD from the University of Southern California, Los Angeles, CA, USA, in 1982. From 1973 to 1987, he had been a lecturer, associate professor and professor of Tatung Institute of Technology. From 1987, he has been a professor, chair professor and Tsing Hua distinguished chair professor with the Department of Electrical Engineering of National Tsing Hua University, Hsinchu, Taiwan. His research interests include robust control theory and engineering design, robust signal processing and communication system design, systems biology and their applications. He has published over 370 journal papers, including 140 papers in control, 80 papers in signal processing and communication and 120 papers in systems biology. He has also published 14 monographs. He was the recipient of numerous awards for his academic accomplishments in robust control, fuzzy control, H∞ control, stochastic control, signal processing and systems biology, including four Outstanding Research Awards of National Science Council, Academic Award in Engineering from Ministry of Education, National Chair Professor of the Ministry of Education and Best Impact Award of IEEE Taiwan Section for his most SCI citations of IEEE members in Taiwan. His current research interest focuses on the H∞ team formation network tracking control of large-scale UAVs, large-scale biped robots and their team cooperation, physics-generated AI-driven robust nonlinear H∞ filter and control designs of nonlinear dynamic systems, systems medicine design via DNN-based DTI model and design specifications. He is a life fellow of IEEE. Professor Chen is a 1% scientist according to the World’s Top 2% Scientists of Stanford University.
"About the title" may belong to another edition of this title.
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