Nonlinear Observers And Applications


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Nonlinear Observers and Applications


Nonlinear Observers and Applications

Author: Gildas Besançon

language: en

Publisher: Springer

Release Date: 2007-10-11


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The purpose of this fantastically useful book is to lay out an overview on possible tools for state reconstruction in nonlinear systems. Here, basic observability notions and observer structures are recalled, together with ingredients for advanced designs on this basis. The problem of state reconstruction in dynamical systems, known as observer problem, is crucial for controlling or even merely monitoring processes. For linear systems, the theory has been well established for several years, so this book attempts to tackle the problem for non-linear systems.

Nonlinear Observers and Applications


Nonlinear Observers and Applications

Author: Gildas Besançon

language: en

Publisher: Springer

Release Date: 2009-09-02


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The purpose of this fantastically useful book is to lay out an overview on possible tools for state reconstruction in nonlinear systems. Here, basic observability notions and observer structures are recalled, together with ingredients for advanced designs on this basis. The problem of state reconstruction in dynamical systems, known as observer problem, is crucial for controlling or even merely monitoring processes. For linear systems, the theory has been well established for several years, so this book attempts to tackle the problem for non-linear systems.

Intelligent Observer and Control Design for Nonlinear Systems


Intelligent Observer and Control Design for Nonlinear Systems

Author: Dierk Schröder

language: en

Publisher: Springer Science & Business Media

Release Date: 2000-01-07


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Control theory of nonlinear systems, in which either the linear part is known but the relevant nonlinearities in place, kind or parameters are unknown, or both the linear and the nonlinear parts are partially or even most unknown, is a new, demanding and highly interesting field. This book treats the problem by focussing on the role of learning. Intelligent learning techniques are able to determine the unknown components of nonlinear systems. These processes are always stable and convergent. The methods presented can be used both on-line and off-line. They have applications in mechatronics, hydraulics and combustion engines.