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

Name: Physics-Generated AIs of Robust Nonlinear Filter and Control Designs for Complicated Man-Made Machines
Price: 298.75 AUD
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Theory and Applications

By: Bor-Sen Chen

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Hardcover | 10 February 2026 | Edition Number 1

At a Glance

Hardcover
416 Pages

Dimensions(cm)
23.4 x 15.6

Hardcover

RRP $347.00

$298.75

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Available: 10th February 2026

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This book introduces a robust Hâ 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 Hâ 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 Hâ or mixed H2/Hâ filter

Applies physics-generated AI-driven robust Hâ or mixed H2/Hâ filter and reference tracking control schemes to the trajectory estimation and reference tracking control of man-made machines

Introduces physics-generated AI-driven decentralized Hâ 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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