Physical Generative AIs of Robust Nonlinear Filter and Control Designs for Complicated Man-Made Machines

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予約

Physical Generative AIs of Robust Nonlinear Filter and Control Designs for Complicated Man-Made Machines

Chen, Bor-Sen

ウェブストア価格 ¥42,979（本体¥39,072）
CRC Press（2026/02発売）
外貨定価 US$ 220.00
ポイント 390pt

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製本 Hardcover:ハードカバー版／ページ数 416 p.
言語 ENG
商品コード 9781041129349

Full Description

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.

1. Introduction to Physical Generative AI-Driven Filter and Control Scheme of Nonlinear Stochastic Systems of Man-Made Machines 2. Physical Generative AI-Driven H∞ Stabilization Control Scheme of Nonlinear Time-Varying Dynamic Systems with Its Application to Quadrotor UAV Tracking Control Design 3. Robust H∞ Physical Generative AI-Driven Filter Design of Nonlinear Stochastic Systems: With Application to Radar Detection of Incoming Missile 4. Physical Generative AI-Driven Mixed H2/H∞ Filter Design of Nonlinear Stochastic Systems for the Trajectory Estimation of Incoming Ballistic Missile 5. Physical Generation AI-Driven Robust H∞ Observer-Based Reference Tracking Control Design of Nonlinear Stochastic Systems with Application to Trajectory Tracking of Quadrotor UAV 6. Physical Generative AI-Driven Mixed H2/H∞ Observer-Based Regulation Control of Nonlinear Stochastic Systems with Application to Anti-Missile Guidance Control System 7. Robust Physical Generative 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. Physical Generative AI-Driven Decentralized H∞ Team Formation Tracking Control for Large-Scale Biped Robots 9. Physical Generative AI-Driven H∞ Decentralized Attack-Tolerant Observer-Based Team Formation Network Control of Large-Scale Quadrotor UAVs 10. Decentralized H∞ Physical Generative AI-Driven Observer-Based Attack-Tolerant Formation Tracking Network Control of Large-Scale LEO Satellites