{"product_id":"9781041129349","title":"Physics-Generated AIs of Robust Nonlinear Filter and Control Designs for Complicated Man-Made Machines","description":"\u003cp\u003eThis 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 machinesThis book is aimed at graduate students and researchers in control science, signal processing and artificial intelligence.\u003c\/p\u003e","brand":"Bor-Sen Chen","offers":[{"title":"Default Title","offer_id":52758206972222,"sku":"9781041129349","price":10920.36,"currency_code":"ZAR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4357\/7662\/files\/9781041129349.jpg?v=1766066593","url":"https:\/\/exclusivebooks.co.za\/products\/9781041129349","provider":"Exclusive Books Online","version":"1.0","type":"link"}