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Physics-Generated AIs of Robust Nonlinear Filter and Control Designs for Complicated Man-Made Machines
Hardcover
ISBN13: 9781041129349
Publisher: CRC Press
Published: Mar 18 2026
Pages: 436
Weight: 1.76
Height: 1.00 Width: 6.14 Depth: 9.21
Language: English
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.
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