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Differential Neural Networks for Robust Nonlinear Control

Differential Neural Networks for Robust Nonlinear Control PDF Author: Alexander S. Poznyak
Publisher: World Scientific
ISBN: 9789812811295
Category : Science
Languages : en
Pages : 422

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Book Description
This book deals with continuous time dynamic neural networks theory applied to the solution of basic problems in robust control theory, including identification, state space estimation (based on neuro-observers) and trajectory tracking. The plants to be identified and controlled are assumed to be a priori unknown but belonging to a given class containing internal unmodelled dynamics and external perturbations as well. The error stability analysis and the corresponding error bounds for different problems are presented. The effectiveness of the suggested approach is illustrated by its application to various controlled physical systems (robotic, chaotic, chemical, etc.). Contents: Theoretical Study: Neural Networks Structures; Nonlinear System Identification: Differential Learning; Sliding Mode Identification: Algebraic Learning; Neural State Estimation; Passivation via Neuro Control; Neuro Trajectory Tracking; Neurocontrol Applications: Neural Control for Chaos; Neuro Control for Robot Manipulators; Identification of Chemical Processes; Neuro Control for Distillation Column; General Conclusions and Future Work; Appendices: Some Useful Mathematical Facts; Elements of Qualitative Theory of ODE; Locally Optimal Control and Optimization. Readership: Graduate students, researchers, academics/lecturers and industrialists in neural networks.

Differential Neural Networks for Robust Nonlinear Control

Differential Neural Networks for Robust Nonlinear Control PDF Author: Alexander S. Poznyak
Publisher: World Scientific
ISBN: 9789812811295
Category : Science
Languages : en
Pages : 422

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Book Description
This book deals with continuous time dynamic neural networks theory applied to the solution of basic problems in robust control theory, including identification, state space estimation (based on neuro-observers) and trajectory tracking. The plants to be identified and controlled are assumed to be a priori unknown but belonging to a given class containing internal unmodelled dynamics and external perturbations as well. The error stability analysis and the corresponding error bounds for different problems are presented. The effectiveness of the suggested approach is illustrated by its application to various controlled physical systems (robotic, chaotic, chemical, etc.). Contents: Theoretical Study: Neural Networks Structures; Nonlinear System Identification: Differential Learning; Sliding Mode Identification: Algebraic Learning; Neural State Estimation; Passivation via Neuro Control; Neuro Trajectory Tracking; Neurocontrol Applications: Neural Control for Chaos; Neuro Control for Robot Manipulators; Identification of Chemical Processes; Neuro Control for Distillation Column; General Conclusions and Future Work; Appendices: Some Useful Mathematical Facts; Elements of Qualitative Theory of ODE; Locally Optimal Control and Optimization. Readership: Graduate students, researchers, academics/lecturers and industrialists in neural networks.

Differential Neural Networks for Robust Nonlinear Control

Differential Neural Networks for Robust Nonlinear Control PDF Author: Alexander S Poznyak
Publisher: World Scientific
ISBN: 9814491020
Category : Computers
Languages : en
Pages : 456

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Book Description
This book deals with continuous time dynamic neural networks theory applied to the solution of basic problems in robust control theory, including identification, state space estimation (based on neuro-observers) and trajectory tracking. The plants to be identified and controlled are assumed to be a priori unknown but belonging to a given class containing internal unmodelled dynamics and external perturbations as well. The error stability analysis and the corresponding error bounds for different problems are presented. The effectiveness of the suggested approach is illustrated by its application to various controlled physical systems (robotic, chaotic, chemical, etc.). Contents:Theoretical Study:Neural Networks StructuresNonlinear System Identification: Differential LearningSliding Mode Identification: Algebraic LearningNeural State EstimationPassivation via Neuro ControlNeuro Trajectory TrackingNeurocontrol Applications:Neural Control for ChaosNeuro Control for Robot ManipulatorsIdentification of Chemical ProcessesNeuro Control for Distillation ColumnGeneral Conclusions and Future WorkAppendices:Some Useful Mathematical FactsElements of Qualitative Theory of ODELocally Optimal Control and Optimization Readership: Graduate students, researchers, academics/lecturers and industrialists in neural networks. Keywords:Dynamic Neural Networks;System Identification;State Estimation;Adaptive Control;Robust Control;Sliding Mode;Chaos Identification and Control;Chemical Process;Lyapunov Method;StabilityReviews:“This book is the result of many years of research and publications by the authors. Overall, it is a good one that could benefit the researchers and practitioners in the field of intelligent nonlinear control systems. Design methods and analytical results are well presented and substantiated by closely-related simulation examples and engineering applications. It is a very good addition to the libraries of those interested in the subject. It is also qualified to be used as a postgraduate-level reference.”International Journal of Adaptive Control and Signal Processing

Mechanical Engineers' Handbook, Volume 2

Mechanical Engineers' Handbook, Volume 2 PDF Author: Myer Kutz
Publisher: Wiley
ISBN: 9780471719861
Category : Technology & Engineering
Languages : en
Pages : 928

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Book Description
The updated Revision of the Bestseller--In a more Useful Format! Mechanical Engineers' Handbook has a long tradition as a single resource of valuable information related to specialty areas in the diverse industries and job functions in which mechanical engineers work. This Third Edition, the most aggressive revision to date, goes beyond the straight data, formulas, and calculations provided in other handbooks and focuses on authoritative discussions, real-world examples, and insightful analyses while covering more topics that in previous editions. Book 2: Instrumentation, Systems, Controls, and MEMS is comprised of two major parts, conveniently put together because feedback control systems require measurement transducers. The first part covers instrumentation, including transducer design, strain gages, flow meters, digital integrated circuits, and issues involved in processing transducer signals and acquiring and displaying data. The second part addresses systems and control, including: * Control system design, analysis, and performance modification * Design of servoactuators, controllers, and general-purpose control devices * "New departures" in mechanical engineering, including neural networks, mechatronics, and MEMS

Artificial Neural Networks for Modelling and Control of Non-Linear Systems

Artificial Neural Networks for Modelling and Control of Non-Linear Systems PDF Author: Johan A.K. Suykens
Publisher: Springer Science & Business Media
ISBN: 9780792396789
Category : Technology & Engineering
Languages : en
Pages : 235

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Book Description
Artificial neural networks possess several properties that make them particularly attractive for applications to modelling and control of complex non-linear systems. Among these properties are their universal approximation ability, their parallel network structure and the availability of on- and off-line learning methods for the interconnection weights. However, dynamic models that contain neural network architectures might be highly non-linear and difficult to analyse as a result. Artificial Neural Networks for Modelling and Control of Non-Linear Systems investigates the subject from a system theoretical point of view. However the mathematical theory that is required from the reader is limited to matrix calculus, basic analysis, differential equations and basic linear system theory. No preliminary knowledge of neural networks is explicitly required. The book presents both classical and novel network architectures and learning algorithms for modelling and control. Topics include non-linear system identification, neural optimal control, top-down model based neural control design and stability analysis of neural control systems. A major contribution of this book is to introduce NLq Theory as an extension towards modern control theory, in order to analyze and synthesize non-linear systems that contain linear together with static non-linear operators that satisfy a sector condition: neural state space control systems are an example. Moreover, it turns out that NLq Theory is unifying with respect to many problems arising in neural networks, systems and control. Examples show that complex non-linear systems can be modelled and controlled within NLq theory, including mastering chaos. The didactic flavor of this book makes it suitable for use as a text for a course on Neural Networks. In addition, researchers and designers will find many important new techniques, in particular NLq emTheory, that have applications in control theory, system theory, circuit theory and Time Series Analysis.

Journal of Dynamic Systems, Measurement, and Control

Journal of Dynamic Systems, Measurement, and Control PDF Author:
Publisher:
ISBN:
Category : Automatic control
Languages : en
Pages :

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The British National Bibliography

The British National Bibliography PDF Author: Arthur James Wells
Publisher:
ISBN:
Category : Bibliography, National
Languages : en
Pages :

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Systems Structure and Control

Systems Structure and Control PDF Author: Petr Husek
Publisher: IntechOpen
ISBN: 9789537619053
Category : Science
Languages : en
Pages : 256

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Book Description
The title of the book System, Structure and Control encompasses broad field of theory and applications of many different control approaches applied on different classes of dynamic systems. Output and state feedback control include among others robust control, optimal control or intelligent control methods such as fuzzy or neural network approach, dynamic systems are e.g. linear or nonlinear with or without time delay, fixed or uncertain, onedimensional or multidimensional. The applications cover all branches of human activities including any kind of industry, economics, biology, social sciences etc.

Nonlinear H2/H-Infinity Constrained Feedback Control

Nonlinear H2/H-Infinity Constrained Feedback Control PDF Author: Murad Abu-Khalaf
Publisher: Springer Science & Business Media
ISBN: 1846283507
Category : Technology & Engineering
Languages : en
Pages : 204

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Book Description
This book provides techniques to produce robust, stable and useable solutions to problems of H-infinity and H2 control in high-performance, non-linear systems for the first time. The book is of importance to control designers working in a variety of industrial systems. Case studies are given and the design of nonlinear control systems of the same caliber as those obtained in recent years using linear optimal and bounded-norm designs is explained.

Decision and Control

Decision and Control PDF Author: IEEE Control Systems Society
Publisher:
ISBN: 9780780312982
Category : Adaptive control systems
Languages : en
Pages : 876

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Absolute Stability of Nonlinear Control Systems

Absolute Stability of Nonlinear Control Systems PDF Author: Xiaoxin Liao
Publisher: Springer Science & Business Media
ISBN: 140208482X
Category : Mathematics
Languages : en
Pages : 384

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Book Description
Following the recent developments in the field of absolute stability, Prof. Xiaoxin Liao, in conjunction with Prof. Pei Yu, has created a second edition of his seminal work on the subject. Liao begins with an introduction to the Lurie problem and Lurie control system, before moving on to the simple algebraic sufficient conditions for the absolute stability of autonomous and non-autonomous ODE systems, as well as several special classes of Lurie-type systems. The focus of the book then shifts toward the new results and research that have appeared in the decade since the first edition was published. This book is aimed to be used by undergraduates in the areas of applied mathematics, nonlinear control systems, and chaos control and synchronisation, but may also be useful as a reference for researchers and engineers. The book is self-contained, though a basic knowledge of calculus, linear system and matrix theory, and ordinary differential equations is a prerequisite.