9781041129349 - Physics-Generated AIs of Robust Nonlinear Filter and Control Designs for Complicated Man-Made Machines de Chen, Bor-Sen (16 resultados)

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Hardcover. Condición: Brand New. 416 pages. 9.18x6.12x9.21 inches. In Stock.

Hardcover. Condición: new. Hardcover. 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 filterApplies 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 machinesIntroduces physics-generated AI-driven decentralized H observer-based team formation tracking control of large-scale quadrotor UAVs, biped robots or LEO satellitesPromulgates the idea of the forthcoming age of physics-generated AI in robotDescribes robust physics-generated AI-driven filter and control schemes for complex man-made machinesThis book is aimed at graduate students and researchers in control science, signal processing and artificial intelligence. 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 item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Hardcover. Condición: new. Hardcover. 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 filterApplies 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 machinesIntroduces physics-generated AI-driven decentralized H observer-based team formation tracking control of large-scale quadrotor UAVs, biped robots or LEO satellitesPromulgates the idea of the forthcoming age of physics-generated AI in robotDescribes robust physics-generated AI-driven filter and control schemes for complex man-made machinesThis book is aimed at graduate students and researchers in control science, signal processing and artificial intelligence. 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 item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

HRD. Condición: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

HRD. Condición: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Buch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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.

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Bor-Sen Chen received his BS in electrical engineering from Tatung Institute of Technology, Taipei, Taiwan, in 1970, and MS in geophysics from National Central University, Chungli, Taiwan, in 1973, and PhD from the University of Southern.

Hardcover. Condición: new. Hardcover. 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 filterApplies 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 machinesIntroduces physics-generated AI-driven decentralized H observer-based team formation tracking control of large-scale quadrotor UAVs, biped robots or LEO satellitesPromulgates the idea of the forthcoming age of physics-generated AI in robotDescribes robust physics-generated AI-driven filter and control schemes for complex man-made machinesThis book is aimed at graduate students and researchers in control science, signal processing and artificial intelligence. 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 item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Buch. Condición: Neu. Physics-Generated AIs of Robust Nonlinear Filter and Control Designs for Complicated Man-Made Machines | Bor-Sen Chen | Buch | Einband - fest (Hardcover) | Englisch | 2026 | CRC Press | EAN 9781041129349 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.