Neural Network Control Of Nonlinear Discrete Time Systems

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Neural Network Control of Nonlinear Discrete-Time Systems

Jagannathan Sarangapani
Neural Network Control of Nonlinear Discrete Time Systems Book PDF

Author : Jagannathan Sarangapani
Publisher : CRC Press
Page : 624 pages
File Size : 41,9 Mb
Release : 2018-10-03
Category : Technology & Engineering
ISBN : 9781420015454

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Neural Network Control of Nonlinear Discrete-Time Systems by Jagannathan Sarangapani Pdf

Intelligent systems are a hallmark of modern feedback control systems. But as these systems mature, we have come to expect higher levels of performance in speed and accuracy in the face of severe nonlinearities, disturbances, unforeseen dynamics, and unstructured uncertainties. Artificial neural networks offer a combination of adaptability, parallel processing, and learning capabilities that outperform other intelligent control methods in more complex systems. Borrowing from Biology Examining neurocontroller design in discrete-time for the first time, Neural Network Control of Nonlinear Discrete-Time Systems presents powerful modern control techniques based on the parallelism and adaptive capabilities of biological nervous systems. At every step, the author derives rigorous stability proofs and presents simulation examples to demonstrate the concepts. Progressive Development After an introduction to neural networks, dynamical systems, control of nonlinear systems, and feedback linearization, the book builds systematically from actuator nonlinearities and strict feedback in nonlinear systems to nonstrict feedback, system identification, model reference adaptive control, and novel optimal control using the Hamilton-Jacobi-Bellman formulation. The author concludes by developing a framework for implementing intelligent control in actual industrial systems using embedded hardware. Neural Network Control of Nonlinear Discrete-Time Systems fosters an understanding of neural network controllers and explains how to build them using detailed derivations, stability analysis, and computer simulations.

Neural Network Control Of Robot Manipulators And Non-Linear Systems

F W Lewis,S. Jagannathan,A Yesildirak
Neural Network Control Of Robot Manipulators And Non Linear Systems Book PDF

Author : F W Lewis,S. Jagannathan,A Yesildirak
Publisher : CRC Press
Page : 470 pages
File Size : 54,7 Mb
Release : 1998-11-30
Category : Technology & Engineering
ISBN : 0748405968

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Neural Network Control Of Robot Manipulators And Non-Linear Systems by F W Lewis,S. Jagannathan,A Yesildirak Pdf

There has been great interest in "universal controllers" that mimic the functions of human processes to learn about the systems they are controlling on-line so that performance improves automatically. Neural network controllers are derived for robot manipulators in a variety of applications including position control, force control, link flexibility stabilization and the management of high-frequency joint and motor dynamics. The first chapter provides a background on neural networks and the second on dynamical systems and control. Chapter three introduces the robot control problem and standard techniques such as torque, adaptive and robust control. Subsequent chapters give design techniques and Stability Proofs For NN Controllers For Robot Arms, Practical Robotic systems with high frequency vibratory modes, force control and a general class of non-linear systems. The last chapters are devoted to discrete- time NN controllers. Throughout the text, worked examples are provided.

Discrete-Time Inverse Optimal Control for Nonlinear Systems

Edgar N. Sanchez,Fernando Ornelas-Tellez
Discrete Time Inverse Optimal Control for Nonlinear Systems Book PDF

Author : Edgar N. Sanchez,Fernando Ornelas-Tellez
Publisher : CRC Press
Page : 268 pages
File Size : 46,7 Mb
Release : 2017-12-19
Category : Technology & Engineering
ISBN : 9781466580886

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Discrete-Time Inverse Optimal Control for Nonlinear Systems by Edgar N. Sanchez,Fernando Ornelas-Tellez Pdf

Discrete-Time Inverse Optimal Control for Nonlinear Systems proposes a novel inverse optimal control scheme for stabilization and trajectory tracking of discrete-time nonlinear systems. This avoids the need to solve the associated Hamilton-Jacobi-Bellman equation and minimizes a cost functional, resulting in a more efficient controller. Design More Efficient Controllers for Stabilization and Trajectory Tracking of Discrete-Time Nonlinear Systems The book presents two approaches for controller synthesis: the first based on passivity theory and the second on a control Lyapunov function (CLF). The synthesized discrete-time optimal controller can be directly implemented in real-time systems. The book also proposes the use of recurrent neural networks to model discrete-time nonlinear systems. Combined with the inverse optimal control approach, such models constitute a powerful tool to deal with uncertainties such as unmodeled dynamics and disturbances. Learn from Simulations and an In-Depth Case Study The authors include a variety of simulations to illustrate the effectiveness of the synthesized controllers for stabilization and trajectory tracking of discrete-time nonlinear systems. An in-depth case study applies the control schemes to glycemic control in patients with type 1 diabetes mellitus, to calculate the adequate insulin delivery rate required to prevent hyperglycemia and hypoglycemia levels. The discrete-time optimal and robust control techniques proposed can be used in a range of industrial applications, from aerospace and energy to biomedical and electromechanical systems. Highlighting optimal and efficient control algorithms, this is a valuable resource for researchers, engineers, and students working in nonlinear system control.

Discrete-Time High Order Neural Control

Edgar N. Sanchez,Alma Y. Alanís,Alexander G. Loukianov
Discrete Time High Order Neural Control Book PDF

Author : Edgar N. Sanchez,Alma Y. Alanís,Alexander G. Loukianov
Publisher : Springer
Page : 110 pages
File Size : 49,9 Mb
Release : 2008-06-24
Category : Technology & Engineering
ISBN : 9783540782896

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Discrete-Time High Order Neural Control by Edgar N. Sanchez,Alma Y. Alanís,Alexander G. Loukianov Pdf

Neural networks have become a well-established methodology as exempli?ed by their applications to identi?cation and control of general nonlinear and complex systems; the use of high order neural networks for modeling and learning has recently increased. Usingneuralnetworks,controlalgorithmscanbedevelopedtoberobustto uncertainties and modeling errors. The most used NN structures are Feedf- ward networks and Recurrent networks. The latter type o?ers a better suited tool to model and control of nonlinear systems. There exist di?erent training algorithms for neural networks, which, h- ever, normally encounter some technical problems such as local minima, slow learning, and high sensitivity to initial conditions, among others. As a viable alternative, new training algorithms, for example, those based on Kalman ?ltering, have been proposed. There already exists publications about trajectory tracking using neural networks; however, most of those works were developed for continuous-time systems. On the other hand, while extensive literature is available for linear discrete-timecontrolsystem,nonlineardiscrete-timecontroldesigntechniques have not been discussed to the same degree. Besides, discrete-time neural networks are better ?tted for real-time implementations.

Discrete-Time High Order Neural Control

Edgar N. Sanchez,Alma Y. Alanís,Alexander G. Loukianov
Discrete Time High Order Neural Control Book PDF

Author : Edgar N. Sanchez,Alma Y. Alanís,Alexander G. Loukianov
Publisher : Springer Science & Business Media
Page : 116 pages
File Size : 50,9 Mb
Release : 2008-04-29
Category : Mathematics
ISBN : 9783540782889

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Discrete-Time High Order Neural Control by Edgar N. Sanchez,Alma Y. Alanís,Alexander G. Loukianov Pdf

Neural networks have become a well-established methodology as exempli?ed by their applications to identi?cation and control of general nonlinear and complex systems; the use of high order neural networks for modeling and learning has recently increased. Usingneuralnetworks,controlalgorithmscanbedevelopedtoberobustto uncertainties and modeling errors. The most used NN structures are Feedf- ward networks and Recurrent networks. The latter type o?ers a better suited tool to model and control of nonlinear systems. There exist di?erent training algorithms for neural networks, which, h- ever, normally encounter some technical problems such as local minima, slow learning, and high sensitivity to initial conditions, among others. As a viable alternative, new training algorithms, for example, those based on Kalman ?ltering, have been proposed. There already exists publications about trajectory tracking using neural networks; however, most of those works were developed for continuous-time systems. On the other hand, while extensive literature is available for linear discrete-timecontrolsystem,nonlineardiscrete-timecontroldesigntechniques have not been discussed to the same degree. Besides, discrete-time neural networks are better ?tted for real-time implementations.

Adaptive Sliding Mode Neural Network Control for Nonlinear Systems

Yang Li,Jianhua Zhang,Wu Qiong
Adaptive Sliding Mode Neural Network Control for Nonlinear Systems Book PDF

Author : Yang Li,Jianhua Zhang,Wu Qiong
Publisher : Academic Press
Page : 186 pages
File Size : 42,8 Mb
Release : 2018-11-16
Category : Technology & Engineering
ISBN : 9780128154328

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Adaptive Sliding Mode Neural Network Control for Nonlinear Systems by Yang Li,Jianhua Zhang,Wu Qiong Pdf

Adaptive Sliding Mode Neural Network Control for Nonlinear Systems introduces nonlinear systems basic knowledge, analysis and control methods, and applications in various fields. It offers instructive examples and simulations, along with the source codes, and provides the basic architecture of control science and engineering. Introduces nonlinear systems' basic knowledge, analysis and control methods, along with applications in various fields Offers instructive examples and simulations, including source codes Provides the basic architecture of control science and engineering

Neural Networks Modeling and Control

Jorge D. Rios,Alma Y. Alanis,Nancy Arana-Daniel,Carlos Lopez-Franco
Neural Networks Modeling and Control Book PDF

Author : Jorge D. Rios,Alma Y. Alanis,Nancy Arana-Daniel,Carlos Lopez-Franco
Publisher : Academic Press
Page : 0 pages
File Size : 50,9 Mb
Release : 2020-01-20
Category : Science
ISBN : 0128170786

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Neural Networks Modeling and Control by Jorge D. Rios,Alma Y. Alanis,Nancy Arana-Daniel,Carlos Lopez-Franco Pdf

Neural Networks Modelling and Control: Applications for Unknown Nonlinear Delayed Systems in Discrete Time focuses on modeling and control of discrete-time unknown nonlinear delayed systems under uncertainties based on Artificial Neural Networks. First, a Recurrent High Order Neural Network (RHONN) is used to identify discrete-time unknown nonlinear delayed systems under uncertainties, then a RHONN is used to design neural observers for the same class of systems. Therefore, both neural models are used to synthesize controllers for trajectory tracking based on two methodologies: sliding mode control and Inverse Optimal Neural Control. As well as considering the different neural control models and complications that are associated with them, this book also analyzes potential applications, prototypes and future trends.

Discrete-Time Recurrent Neural Control

Edgar N. Sanchez
Discrete Time Recurrent Neural Control Book PDF

Author : Edgar N. Sanchez
Publisher : CRC Press
Page : 292 pages
File Size : 49,9 Mb
Release : 2018-09-03
Category : Technology & Engineering
ISBN : 9781351377430

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Discrete-Time Recurrent Neural Control by Edgar N. Sanchez Pdf

The book presents recent advances in the theory of neural control for discrete-time nonlinear systems with multiple inputs and multiple outputs. The simulation results that appear in each chapter include rigorous mathematical analyses, based on the Lyapunov approach, to establish its properties. The book contains two sections: the first focuses on the analyses of control techniques; the second is dedicated to illustrating results of real-time applications. It also provides solutions for the output trajectory tracking problem of unknown nonlinear systems based on sliding modes and inverse optimal control scheme. "This book on Discrete-time Recurrent Neural Control is unique in the literature, with new knowledge and information about the new technique of recurrent neural control especially for discrete-time systems. The book is well organized and clearly presented. It will be welcome by a wide range of researchers in science and engineering, especially graduate students and junior researchers who want to learn the new notion of recurrent neural control. I believe it will have a good market. It is an excellent book after all." — Guanrong Chen, City University of Hong Kong "This book includes very relevant topics, about neural control. In these days, Artificial Neural Networks have been recovering their relevance and well-stablished importance, this due to its great capacity to process big amounts of data. Artificial Neural Networks development always is related to technological advancements; therefore, it is not a surprise that now we are being witnesses of this new era in Artificial Neural Networks, however most of the developments in this research area only focuses on applicability of the proposed schemes. However, Edgar N. Sanchez author of this book does not lose focus and include both important applications as well as a deep theoretical analysis of Artificial Neural Networks to control discrete-time nonlinear systems. It is important to remark that first, the considered Artificial Neural Networks are development in discrete-time this simplify its implementation in real-time; secondly, the proposed applications ranging from modelling of unknown discrete-time on linear systems to control electrical machines with an emphasize to renewable energy systems. However, its applications are not limited to these kind of systems, due to their theoretical foundation it can be applicable to a large class of nonlinear systems. All of these is supported by the solid research done by the author." — Alma Y. Alanis, University of Guadalajara, Mexico "This book discusses in detail; how neural networks can be used for optimal as well as robust control design. Design of neural network controllers for real time applications such as induction motors, boost converters, inverted pendulum and doubly fed induction generators has also been carried out which gives the book an edge over other similar titles. This book will be an asset for the novice to the experienced ones." — Rajesh Joseph Abraham, Indian Institute of Space Science & Technology, Thiruvananthapuram, India

Neural Network Control Of Robot Manipulators And Non-Linear Systems

F W Lewis,S. Jagannathan,A Yesildirak
Neural Network Control Of Robot Manipulators And Non Linear Systems Book PDF

Author : F W Lewis,S. Jagannathan,A Yesildirak
Publisher : CRC Press
Page : 468 pages
File Size : 41,7 Mb
Release : 2020-08-14
Category : Technology & Engineering
ISBN : 9781000162776

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Neural Network Control Of Robot Manipulators And Non-Linear Systems by F W Lewis,S. Jagannathan,A Yesildirak Pdf

There has been great interest in "universal controllers" that mimic the functions of human processes to learn about the systems they are controlling on-line so that performance improves automatically. Neural network controllers are derived for robot manipulators in a variety of applications including position control, force control, link flexibility stabilization and the management of high-frequency joint and motor dynamics. The first chapter provides a background on neural networks and the second on dynamical systems and control. Chapter three introduces the robot control problem and standard techniques such as torque, adaptive and robust control. Subsequent chapters give design techniques and Stability Proofs For NN Controllers For Robot Arms, Practical Robotic systems with high frequency vibratory modes, force control and a general class of non-linear systems. The last chapters are devoted to discrete- time NN controllers. Throughout the text, worked examples are provided.

Recent Advances in Intelligent Control Systems

Wen Yu
Recent Advances in Intelligent Control Systems Book PDF

Author : Wen Yu
Publisher : Springer Science & Business Media
Page : 381 pages
File Size : 43,8 Mb
Release : 2009-05-27
Category : Technology & Engineering
ISBN : 9781848825482

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Recent Advances in Intelligent Control Systems by Wen Yu Pdf

"Recent Advances in Intelligent Control Systems" gathers contributions from workers around the world and presents them in four categories according to the style of control employed: fuzzy control; neural control; fuzzy neural control; and intelligent control. The contributions illustrate the interdisciplinary antecedents of intelligent control and contrast its results with those of more traditional control methods. A variety of design examples, drawn primarily from robotics and mechatronics but also representing process and production engineering, large civil structures, network flows, and others, provide instances of the application of computational intelligence for control. Presenting state-of-the-art research, this collection will be of benefit to researchers in automatic control, automation, computer science (especially artificial intelligence) and mechatronics while graduate students and practicing control engineers working with intelligent systems will find it a good source of study material.

Discrete-Time Recurrent Neural Control

Edgar N. Sanchez
Discrete Time Recurrent Neural Control Book PDF

Author : Edgar N. Sanchez
Publisher : CRC Press
Page : 205 pages
File Size : 46,5 Mb
Release : 2018-09-03
Category : Technology & Engineering
ISBN : 9781351377423

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Discrete-Time Recurrent Neural Control by Edgar N. Sanchez Pdf

The book presents recent advances in the theory of neural control for discrete-time nonlinear systems with multiple inputs and multiple outputs. The simulation results that appear in each chapter include rigorous mathematical analyses, based on the Lyapunov approach, to establish its properties. The book contains two sections: the first focuses on the analyses of control techniques; the second is dedicated to illustrating results of real-time applications. It also provides solutions for the output trajectory tracking problem of unknown nonlinear systems based on sliding modes and inverse optimal control scheme. "This book on Discrete-time Recurrent Neural Control is unique in the literature, with new knowledge and information about the new technique of recurrent neural control especially for discrete-time systems. The book is well organized and clearly presented. It will be welcome by a wide range of researchers in science and engineering, especially graduate students and junior researchers who want to learn the new notion of recurrent neural control. I believe it will have a good market. It is an excellent book after all." — Guanrong Chen, City University of Hong Kong "This book includes very relevant topics, about neural control. In these days, Artificial Neural Networks have been recovering their relevance and well-stablished importance, this due to its great capacity to process big amounts of data. Artificial Neural Networks development always is related to technological advancements; therefore, it is not a surprise that now we are being witnesses of this new era in Artificial Neural Networks, however most of the developments in this research area only focuses on applicability of the proposed schemes. However, Edgar N. Sanchez author of this book does not lose focus and include both important applications as well as a deep theoretical analysis of Artificial Neural Networks to control discrete-time nonlinear systems. It is important to remark that first, the considered Artificial Neural Networks are development in discrete-time this simplify its implementation in real-time; secondly, the proposed applications ranging from modelling of unknown discrete-time on linear systems to control electrical machines with an emphasize to renewable energy systems. However, its applications are not limited to these kind of systems, due to their theoretical foundation it can be applicable to a large class of nonlinear systems. All of these is supported by the solid research done by the author." — Alma Y. Alanis, University of Guadalajara, Mexico "This book discusses in detail; how neural networks can be used for optimal as well as robust control design. Design of neural network controllers for real time applications such as induction motors, boost converters, inverted pendulum and doubly fed induction generators has also been carried out which gives the book an edge over other similar titles. This book will be an asset for the novice to the experienced ones." — Rajesh Joseph Abraham, Indian Institute of Space Science & Technology, Thiruvananthapuram, India

Nonlinear Dynamical Systems

Irwin W. Sandberg,James T. Lo,Craig L. Fancourt,José C. Principe,Shigeru Katagiri,Simon Haykin
Nonlinear Dynamical Systems Book PDF

Author : Irwin W. Sandberg,James T. Lo,Craig L. Fancourt,José C. Principe,Shigeru Katagiri,Simon Haykin
Publisher : John Wiley & Sons
Page : 316 pages
File Size : 43,8 Mb
Release : 2001-02-21
Category : Technology & Engineering
ISBN : 0471349119

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Nonlinear Dynamical Systems by Irwin W. Sandberg,James T. Lo,Craig L. Fancourt,José C. Principe,Shigeru Katagiri,Simon Haykin Pdf

Sechs erfahrene Autoren beschreiben in diesem Band ein Spezialgebiet der neuronalen Netze mit Anwendungen in der Signalsteuerung, Signalverarbeitung und Zeitreihenanalyse. Ein zeitgemäßer Beitrag zur Behandlung nichtlinear-dynamischer Systeme!

Robust Discrete-Time Flight Control of UAV with External Disturbances

Shuyi Shao,Mou Chen,Peng Shi
Robust Discrete Time Flight Control of UAV with External Disturbances Book PDF

Author : Shuyi Shao,Mou Chen,Peng Shi
Publisher : Springer Nature
Page : 207 pages
File Size : 43,5 Mb
Release : 2020-09-26
Category : Technology & Engineering
ISBN : 9783030579579

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Robust Discrete-Time Flight Control of UAV with External Disturbances by Shuyi Shao,Mou Chen,Peng Shi Pdf

This book studies selected discrete-time flight control schemes for fixed-wing unmanned aerial vehicle (UAV) systems in the presence of system uncertainties, external disturbances and input saturation. The main contributions of this book for UAV systems are as follows: (i) the proposed integer-order discrete-time control schemes are based on the designed discrete-time disturbance observers (DTDOs) and the neural network (NN); and (ii) the fractional-order discrete-time control schemes are developed by using the fractional-order calculus theory, the NN and the DTDOs. The book offers readers a good understanding of how to establish discrete-time tracking control schemes for fixed-wing UAV systems subject to system uncertainties, external wind disturbances and input saturation. It represents a valuable reference guide for academic research on uncertain UAV systems, and can also support advanced / Ph.D. studies on control theory and engineering.

Neural Systems for Control

Omid Omidvar,David L. Elliott
Neural Systems for Control Book PDF

Author : Omid Omidvar,David L. Elliott
Publisher : Elsevier
Page : 358 pages
File Size : 40,6 Mb
Release : 1997-02-24
Category : Computers
ISBN : 0080537391

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Neural Systems for Control by Omid Omidvar,David L. Elliott Pdf

Control problems offer an industrially important application and a guide to understanding control systems for those working in Neural Networks. Neural Systems for Control represents the most up-to-date developments in the rapidly growing aplication area of neural networks and focuses on research in natural and artifical neural systems directly applicable to control or making use of modern control theory. The book covers such important new developments in control systems such as intelligent sensors in semiconductor wafer manufacturing; the relation between muscles and cerebral neurons in speech recognition; online compensation of reconfigurable control for spacecraft aircraft and other systems; applications to rolling mills, robotics and process control; the usage of past output data to identify nonlinear systems by neural networks; neural approximate optimal control; model-free nonlinear control; and neural control based on a regulation of physiological investigation/blood pressure control. All researchers and students dealing with control systems will find the fascinating Neural Systems for Control of immense interest and assistance. Focuses on research in natural and artifical neural systems directly applicable to contol or making use of modern control theory Represents the most up-to-date developments in this rapidly growing application area of neural networks Takes a new and novel approach to system identification and synthesis

Advances in Neural Networks- ISNN 2013

Chengan Guo,Zeng-Guang Hou,Zhigang Zeng
Advances in Neural Networks ISNN 2013 Book PDF

Author : Chengan Guo,Zeng-Guang Hou,Zhigang Zeng
Publisher : Springer
Page : 653 pages
File Size : 43,6 Mb
Release : 2013-07-04
Category : Computers
ISBN : 9783642390685

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Advances in Neural Networks- ISNN 2013 by Chengan Guo,Zeng-Guang Hou,Zhigang Zeng Pdf

The two-volume set LNCS 7951 and 7952 constitutes the refereed proceedings of the 10th International Symposium on Neural Networks, ISNN 2013, held in Dalian, China, in July 2013. The 157 revised full papers presented were carefully reviewed and selected from numerous submissions. The papers are organized in following topics: computational neuroscience, cognitive science, neural network models, learning algorithms, stability and convergence analysis, kernel methods, large margin methods and SVM, optimization algorithms, varational methods, control, robotics, bioinformatics and biomedical engineering, brain-like systems and brain-computer interfaces, data mining and knowledge discovery and other applications of neural networks.