Physics-Generated Artificial Intelligence: Theory and Applications - Chen, Bor-Sen
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Erscheint vorauss. 11. März 2026
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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. Additionally, it presents a method for training deep neural networks (DNNs) using these models, alongside a physical generative AI-driven observer-based reference tracking control scheme, with applications in the guidance and control of relevant systems. Key features-…mehr

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Produktbeschreibung
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. Additionally, it presents a method for training deep neural networks (DNNs) using these models, alongside a physical generative 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 physical generative AI-driven H¿ or mixed H2/H¿ filter -Applies physical generative 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 physical generative 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 physical generative AI in robot -Describes robust physical generative 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.
Autorenporträt
Bor-Sen Chen received B.S. degree in electrical engineering from Tatung Institute of Technology, Taipei, Taiwan, in 1970, and M.S. degree of geophysics from the National Central University, Chungli, Taiwan in 1973, and Ph.D degree from 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 more than 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 4 Outstanding Research Awards of National Science Council, Academic Award in Engineering from Ministry of Education, National Chair Professor of the Ministry of Education, Best Impact Award of IEEE Taiwan Section for his most SCI citations of IEEE members in Taiwan, etc. 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, physical generative AIs-driven robust nonlinear H¿ filter and control designs of nonlinear dynamic systems, systems medicine design via DNN-based DTI model and design specifications, etc. He is a life fellow of IEEE.