Neural Network Engineering In Dynamic Control Systems
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Neural Network Engineering In Dynamic Control Systems
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Author : Kenneth J. Hunt
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Neural Network Engineering In Dynamic Control Systems written by Kenneth J. Hunt and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Technology & Engineering categories.
The series Advances in Industrial Control aims to report and encourage technology transfer in control engineering. The rapid development of control technology impacts all areas of the control discipline. New theory, new controllers, actuators, sensors, new industrial processes, computer methods, new applications, new philosophies, .... , new challenges. Much of this development work resides in industrial reports, feasibility study papers and the reports of advanced collaborative projects. The series offers an opportunity for researchers to present an extended exposition of such new work in all aspects of industrial control for wider and rapid dissemination. Within the control community there has been much discussion of and interest in the new Emerging Technologies and Methods. Neural networks along with Fuzzy Logic and Expert Systems is an emerging methodology which has the potential to contribute to the development of intelligent control technologies. This volume of some thirteen chapters edited by Kenneth Hunt, George Irwin and Kevin Warwick makes a useful contribution to the literature of neural network methods and applications. The chapters are arranged systematically progressing from theoretical foundations, through the training aspects of neural nets and concluding with four chapters of applications. The applications include problems as diverse as oven tempera ture control, and energy/load forecasting routines. We hope this interesting but balanced mix of material appeals to a wide range of readers from the theoretician to the industrial applications engineer.
Neural Network Applications In Control
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Author : George William Irwin
language : en
Publisher: IET
Release Date : 1995
Neural Network Applications In Control written by George William Irwin and has been published by IET this book supported file pdf, txt, epub, kindle and other format this book has been release on 1995 with Computers categories.
The aim is to present an introduction to, and an overview of, the present state of neural network research and development, with an emphasis on control systems application studies. The book is useful to a range of levels of reader. The earlier chapters introduce the more popular networks and the fundamental control principles, these are followed by a series of application studies, most of which are industrially based, and the book concludes with a consideration of some recent research.
Neural Network Control Of Robot Manipulators And Non Linear Systems
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Author : F W Lewis
language : en
Publisher: CRC Press
Release Date : 2020-08-13
Neural Network Control Of Robot Manipulators And Non Linear Systems written by F W Lewis and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-08-13 with Technology & Engineering categories.
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.
High Level Feedback Control With Neural Networks
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Author : Young Ho Kim
language : en
Publisher: World Scientific
Release Date : 1998-09-28
High Level Feedback Control With Neural Networks written by Young Ho Kim and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998-09-28 with Technology & Engineering categories.
Complex industrial or robotic systems with uncertainty and disturbances are difficult to control. As system uncertainty or performance requirements increase, it becomes necessary to augment traditional feedback controllers with additional feedback loops that effectively “add intelligence” to the system. Some theories of artificial intelligence (AI) are now showing how complex machine systems should mimic human cognitive and biological processes to improve their capabilities for dealing with uncertainty.This book bridges the gap between feedback control and AI. It provides design techniques for “high-level” neural-network feedback-control topologies that contain servo-level feedback-control loops as well as AI decision and training at the higher levels. Several advanced feedback topologies containing neural networks are presented, including “dynamic output feedback”, “reinforcement learning” and “optimal design”, as well as a “fuzzy-logic reinforcement” controller. The control topologies are intuitive, yet are derived using sound mathematical principles where proofs of stability are given so that closed-loop performance can be relied upon in using these control systems. Computer-simulation examples are given to illustrate the performance.
Neural Systems For Control
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Author : Omid Omidvar
language : en
Publisher: Elsevier
Release Date : 1997-02-24
Neural Systems For Control written by Omid Omidvar and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 1997-02-24 with Computers categories.
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
Applications Of Neural Adaptive Control Technology
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Author : Jens Kalkkuhl
language : en
Publisher: World Scientific
Release Date : 1997
Applications Of Neural Adaptive Control Technology written by Jens Kalkkuhl and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 1997 with Technology & Engineering categories.
This book presents the results of the second workshop on Neural Adaptive Control Technology, NACT II, held on September 9-10, 1996, in Berlin. The workshop was organised in connection with a three-year European-Union-funded Basic Research Project in the ESPRIT framework, called NACT, a collaboration between Daimler-Benz (Germany) and the University of Glasgow (Scotland).The NACT project, which began on 1 April 1994, is a study of the fundamental properties of neural-network-based adaptive control systems. Where possible, links with traditional adaptive control systems are exploited. A major aim is to develop a systematic engineering procedure for designing neural controllers for nonlinear dynamic systems. The techniques developed are being evaluated on concrete industrial problems from within the Daimler-Benz group of companies.The aim of the workshop was to bring together selected invited specialists in the fields of adaptive control, nonlinear systems and neural networks. The first workshop (NACT I) took place in Glasgow in May 1995 and was mainly devoted to theoretical issues of neural adaptive control. Besides monitoring further development of theory, the NACT II workshop was focused on industrial applications and software tools. This context dictated the focus of the book and guided the editors in the choice of the papers and their subsequent reshaping into substantive book chapters. Thus, with the project having progressed into its applications stage, emphasis is put on the transfer of theory of neural adaptive engineering into industrial practice. The contributors are therefore both renowned academics and practitioners from major industrial users of neurocontrol.
Control And Dynamic Systems
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Author : Cornelius T. Leondes
language : en
Publisher:
Release Date : 1973
Control And Dynamic Systems written by Cornelius T. Leondes and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1973 with Advances in control systems categories.
Neural Networks For Identification Prediction And Control
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Author : D. T. Pham
language : en
Publisher:
Release Date : 1995
Neural Networks For Identification Prediction And Control written by D. T. Pham and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1995 with Neural networks (Computer science) categories.
This publication describes examples of applications of neural networks in modelling, prediction and control. Topics covered include identification of general linear and nonlinear processes, forecasting of river levels, stock market prices, currency exchange rates, and control of a time-delayed plant and a two-joint robot. The neural network types considered are the multilayer perceptron (MLP), the Elman and Jordan networks, the Group-Method-of-Data-Handling (GMDH), the cerebellar-model-articulation-controller (CMAC) networks and neuromorphic fuzzy logic systems. The algorithms presented are the standard backpropagation (BP) algorithm, the Widrow-Hoff learning, dynamic BP and evolutionary learning. Full listings of computer programs written in C for neural-network-based system identification and prediction to facilitate practical experimentation with neural network techniques are included.
Stable Adaptive Neural Network Control
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Author : S.S. Ge
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-03-09
Stable Adaptive Neural Network Control written by S.S. Ge and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-03-09 with Science categories.
Recent years have seen a rapid development of neural network control tech niques and their successful applications. Numerous simulation studies and actual industrial implementations show that artificial neural network is a good candidate for function approximation and control system design in solving the control problems of complex nonlinear systems in the presence of different kinds of uncertainties. Many control approaches/methods, reporting inventions and control applications within the fields of adaptive control, neural control and fuzzy systems, have been published in various books, journals and conference proceedings. In spite of these remarkable advances in neural control field, due to the complexity of nonlinear systems, the present research on adaptive neural control is still focused on the development of fundamental methodologies. From a theoretical viewpoint, there is, in general, lack of a firmly mathematical basis in stability, robustness, and performance analysis of neural network adaptive control systems. This book is motivated by the need for systematic design approaches for stable adaptive control using approximation-based techniques. The main objec tives of the book are to develop stable adaptive neural control strategies, and to perform transient performance analysis of the resulted neural control systems analytically. Other linear-in-the-parameter function approximators can replace the linear-in-the-parameter neural networks in the controllers presented in the book without any difficulty, which include polynomials, splines, fuzzy systems, wavelet networks, among others. Stability is one of the most important issues being concerned if an adaptive neural network controller is to be used in practical applications.
Artificial Neural Networks For Modelling And Control Of Non Linear Systems
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Author : Johan A.K. Suykens
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Artificial Neural Networks For Modelling And Control Of Non Linear Systems written by Johan A.K. Suykens and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Technology & Engineering categories.
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 NLqemTheory, that have applications in control theory, system theory, circuit theory and Time Series Analysis.