Physics-Generated AIs of Robust Nonlinear Filter and Control Designs for Complicated Man-Made Machines
CRC Press (Verlag)
978-1-041-12934-9 (ISBN)
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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.
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.
1. Introduction to Physics-Generated AI-Driven Filter and Control Scheme of Nonlinear Stochastic Systems of Man-Made Machines 2. Physics-Generated AI-Driven H∞ Stabilization Control Scheme of Nonlinear Time-Varying Dynamic Systems with Its Application to Quadrotor UAV Tracking Control Design 3. Robust H∞ Physics-Generated AI-Driven Filter Design of Nonlinear Stochastic Systems: with Application to Radar Detection of Incoming Missile 4. Physics-Generated AI-Driven Mixed H2/H∞ Filter Design of Nonlinear Stochastic Systems for the Trajectory Estimation of Incoming Ballistic Missile 5. Physics-Generated AI-Driven Robust H∞ Observer-Based Reference Tracking Control Design of Nonlinear Stochastic Systems with Application to Reference Trajectory Estimation and Tracking Control of Quadrotor UAV 6. Physics-Generated AI-Driven H2/H∞ Observer-Based State Regulation Control for Nonlinear Stochastic Systems with Application to Anti-Missile Guidance Control System 7. Robust Physics-Generated AI-Driven H∞ Attack-Tolerant Localization Filter-Based Path Tracking Control Design of Mobile Robot via Wireless Sensor Networks in the Intelligent Buildings and Smart Cities 8. Physics-Generated AI-Driven Decentralized H∞ Team Formation Tracking Control for Large-Scale Biped Robots 9. Physics-Generated AI-Driven H∞ Decentralized Attack-Tolerant Observer-Based Team Formation Network Control System of Large-Scale Quadrotor UAVs 10. Decentralized H∞ Physics-Generated AI-Driven Observer-Based Attack-Tolerant Formation Tracking Network Control of Large-Scale LEO Satellites
| Erscheint lt. Verlag | 18.3.2026 |
|---|---|
| Zusatzinfo | 8 Tables, black and white; 77 Line drawings, color; 48 Line drawings, black and white; 77 Illustrations, color; 48 Illustrations, black and white |
| Verlagsort | London |
| Sprache | englisch |
| Maße | 156 x 234 mm |
| Themenwelt | Technik ► Elektrotechnik / Energietechnik |
| Technik ► Nachrichtentechnik | |
| ISBN-10 | 1-041-12934-3 / 1041129343 |
| ISBN-13 | 978-1-041-12934-9 / 9781041129349 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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