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