Physics Generated Artificial Intelligence: Theory and Applications

Name: Physics Generated Artificial Intelligence: Theory and Applications
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Bol 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. 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.

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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. 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.

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