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

Bor-Sen Chen

• Publisher: CRC Press
• Year: 2026
• Print ISBN:
  9781041129349
• eBook ISBN:
  9781040748657
• Edition: 1
• Topic: Technology & Engineering
• Index: Technology & Engineering

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.

Table of contents

• Cover
• Half-Title
• Title
• Copyright
• Contents
• About the Author
• Preface
• Chapter 1 Introduction to Physics-Generated AI-Driven Filter and Control Scheme of Nonlinear Stochastic Systems of Man-Made Machines
• Chapter 2 Physics-Generated AI-Driven H∞ Stabilization Control Scheme of Nonlinear Time-Varying Dynamic Systems with Its Application to Quadrotor UAV Tracking Control Design
• Chapter 3 Robust H∞ Physics-Generated AI-Driven Filter Design of Nonlinear Stochastic Systems: with Application to Radar Detection of Incoming Missile
• Chapter 4 Physics-Generated AI-Driven Mixed H2/H∞ Filter Design of Nonlinear Stochastic Systems for the Trajectory Estimation of Incoming Ballistic Missile
• Chapter 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
• Chapter 6 Physics-Generated AI-Driven H2/H∞ Observer-Based State Regulation Control for Nonlinear Stochastic Systems with Application to Anti-Missile Guidance Control System
• Chapter 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
• Chapter 8 Physics-Generated AI-Driven Decentralized H∞ Team Formation Tracking Control for Large-Scale Biped Robots
• Chapter 9 Physics-Generated AI-Driven H∞ Decentralized Attack-Tolerant Observer-Based Team Formation Network Control System of Large-Scale Quadrotor UAVs
• Chapter 10 Decentralized H∞ Physics-Generated AI-Driven Observer-Based Attack-Tolerant Formation Tracking Network Control of Large-Scale LEO Satellites
• Index