
Physics-Generated AIs of Robust Nonlinear Filter and Control Designs for Complicated Man-Made Machines
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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.
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