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Neural Adaptive Control Technology
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Book Synopsis Neural Adaptive Control Technology by : Rafal Zbikowski
Download or read book Neural Adaptive Control Technology written by Rafal Zbikowski and published by World Scientific. This book was released on 1996-04-13 with total page 357 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is an outgrowth of the workshop on Neural Adaptive Control Technology, NACT I, held in 1995 in Glasgow. Selected workshop participants were asked to substantially expand and revise their contributions to make them into full papers.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). A major aim of the NACT project 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 Daimler-Benz.In the book emphasis is put on development of sound theory of neural adaptive control for nonlinear control systems, but firmly anchored in the engineering context of industrial practice. Therefore the contributors are both renowned academics and practitioners from major industrial users of neurocontrol.
Book Synopsis Adaptive Control with Recurrent High-order Neural Networks by : George A. Rovithakis
Download or read book Adaptive Control with Recurrent High-order Neural Networks written by George A. Rovithakis and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 203 pages. Available in PDF, EPUB and Kindle. Book excerpt: The series Advances in Industrial Control aims to report and encourage technology transfer in control engineering. The rapid development of control technology has an impact on 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. Neural networks is one of those areas where an initial burst of enthusiasm and optimism leads to an explosion of papers in the journals and many presentations at conferences but it is only in the last decade that significant theoretical work on stability, convergence and robustness for the use of neural networks in control systems has been tackled. George Rovithakis and Manolis Christodoulou have been interested in these theoretical problems and in the practical aspects of neural network applications to industrial problems. This very welcome addition to the Advances in Industrial Control series provides a succinct report of their research. The neural network model at the core of their work is the Recurrent High Order Neural Network (RHONN) and a complete theoretical and simulation development is presented. Different readers will find different aspects of the development of interest. The last chapter of the monograph discusses the problem of manufacturing or production process scheduling.
Book Synopsis Neural Adaptive Control Technology by : Rafa? ?bikowski
Download or read book Neural Adaptive Control Technology written by Rafa? ?bikowski and published by World Scientific. This book was released on 1996 with total page 368 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is an outgrowth of the workshop on Neural Adaptive Control Technology, NACT I, held in 1995 in Glasgow. Selected workshop participants were asked to substantially expand and revise their contributions to make them into full papers.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). A major aim of the NACT project 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 Daimler-Benz.In the book emphasis is put on development of sound theory of neural adaptive control for nonlinear control systems, but firmly anchored in the engineering context of industrial practice. Therefore the contributors are both renowned academics and practitioners from major industrial users of neurocontrol.
Book Synopsis Applications Of Neural Adaptive Control Technology by : Andrzej Dzielinski
Download or read book Applications Of Neural Adaptive Control Technology written by Andrzej Dzielinski and published by World Scientific. This book was released on 1997-09-02 with total page 318 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Neural Adaptive Control Technology by : Rafał Żbikowski
Download or read book Neural Adaptive Control Technology written by Rafał Żbikowski and published by . This book was released on 1996 with total page 347 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is an outgrowth of the workshop on Neural Adaptive Control Technology, NACT I, held in 1995 in Glasgow. Selected workshop participants were asked to substantially expand and revise their contributions to make them into full papers.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). A major aim of the NACT project 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 Daimler-Benz.In the book emphasis is put on development of sound theory of neural adaptive control for nonlinear control systems, but firmly anchored in the engineering context of industrial practice. Therefore the contributors are both renowned academics and practitioners from major industrial users of neurocontrol.
Book Synopsis Adaptive Sliding Mode Neural Network Control for Nonlinear Systems by : Yang Li
Download or read book Adaptive Sliding Mode Neural Network Control for Nonlinear Systems written by Yang Li and published by Academic Press. This book was released on 2018-11-16 with total page 186 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Book Synopsis Stable Adaptive Neural Network Control by : S.S. Ge
Download or read book Stable Adaptive Neural Network Control written by S.S. Ge and published by Springer Science & Business Media. This book was released on 2013-03-09 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Neural Network-Based Adaptive Control of Uncertain Nonlinear Systems by : Kasra Esfandiari
Download or read book Neural Network-Based Adaptive Control of Uncertain Nonlinear Systems written by Kasra Esfandiari and published by Springer Nature. This book was released on 2021-06-18 with total page 181 pages. Available in PDF, EPUB and Kindle. Book excerpt: The focus of this book is the application of artificial neural networks in uncertain dynamical systems. It explains how to use neural networks in concert with adaptive techniques for system identification, state estimation, and control problems. The authors begin with a brief historical overview of adaptive control, followed by a review of mathematical preliminaries. In the subsequent chapters, they present several neural network-based control schemes. Each chapter starts with a concise introduction to the problem under study, and a neural network-based control strategy is designed for the simplest case scenario. After these designs are discussed, different practical limitations (i.e., saturation constraints and unavailability of all system states) are gradually added, and other control schemes are developed based on the primary scenario. Through these exercises, the authors present structures that not only provide mathematical tools for navigating control problems, but also supply solutions that are pertinent to real-life systems.
Book Synopsis Evolutionary Learning Algorithms for Neural Adaptive Control by : Dimitris C. Dracopoulos
Download or read book Evolutionary Learning Algorithms for Neural Adaptive Control written by Dimitris C. Dracopoulos and published by Springer. This book was released on 2013-12-21 with total page 214 pages. Available in PDF, EPUB and Kindle. Book excerpt: Evolutionary Learning Algorithms for Neural Adaptive Control is an advanced textbook, which investigates how neural networks and genetic algorithms can be applied to difficult adaptive control problems which conventional results are either unable to solve , or for which they can not provide satisfactory results. It focuses on the principles involved, rather than on the modelling of the applications themselves, and therefore provides the reader with a good introduction to the fundamental issues involved.
Book Synopsis Neural Systems for Control by : Omid Omidvar
Download or read book Neural Systems for Control written by Omid Omidvar and published by Elsevier. This book was released on 1997-02-24 with total page 375 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Book Synopsis Stable Adaptive Control and Estimation for Nonlinear Systems by : Jeffrey T. Spooner
Download or read book Stable Adaptive Control and Estimation for Nonlinear Systems written by Jeffrey T. Spooner and published by John Wiley & Sons. This book was released on 2004-04-07 with total page 564 pages. Available in PDF, EPUB and Kindle. Book excerpt: Thema dieses Buches ist die Anwendung neuronaler Netze und Fuzzy-Logic-Methoden zur Identifikation und Steuerung nichtlinear-dynamischer Systeme. Dabei werden fortgeschrittene Konzepte der herkömmlichen Steuerungstheorie mit den intuitiven Eigenschaften intelligenter Systeme kombiniert, um praxisrelevante Steuerungsaufgaben zu lösen. Die Autoren bieten viel Hintergrundmaterial; ausgearbeitete Beispiele und Übungsaufgaben helfen Studenten und Praktikern beim Vertiefen des Stoffes. Lösungen zu den Aufgaben sowie MATLAB-Codebeispiele sind ebenfalls enthalten.
Book Synopsis Neural Networks for Control by : W. Thomas Miller
Download or read book Neural Networks for Control written by W. Thomas Miller and published by MIT Press. This book was released on 1995 with total page 548 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural Networks for Control brings together examples of all the most important paradigms for the application of neural networks to robotics and control. Primarily concerned with engineering problems and approaches to their solution through neurocomputing systems, the book is divided into three sections: general principles, motion control, and applications domains (with evaluations of the possible applications by experts in the applications areas.) Special emphasis is placed on designs based on optimization or reinforcement, which will become increasingly important as researchers address more complex engineering challenges or real biological-control problems.A Bradford Book. Neural Network Modeling and Connectionism series
Book Synopsis Application of Neural Networks to Adaptive Control of Nonlinear Systems by : Gee Wah Ng
Download or read book Application of Neural Networks to Adaptive Control of Nonlinear Systems written by Gee Wah Ng and published by . This book was released on 1997 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book investigates the ability of a neural network (NN) to learn how to control an unknown (nonlinear, in general) system, using data acquired on-line, that is during the process of attempting to exert control. Two algorithms are developed to train the neural network for real-time control applications. The first algorithm is known as Learning by Recursive Least Squares (LRLS) algorithm and the second algorithm is known as Integrated Gradient and Least Squares (IGLS) algorithm. The ability of these algorithms to train the NN controller for real-time control is demonstrated on practical applications and the local convergence and stability requirements of these algorithms are analysed. In addition, network topology, learning algorithms (particularly supervised learning) and neural network control strategies are presented.
Book Synopsis Neuroscience: From Neural Networks to Artificial Intelligence by : Pablo Rudomin
Download or read book Neuroscience: From Neural Networks to Artificial Intelligence written by Pablo Rudomin and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 588 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Central Nervous System can be considered as an aggregate of neurons specialized in both the transmission and transformation of information. Information can be used for many purposes, but probably the most important one is to generate a representation of the "external" world that allows the organism to react properly to changes in its external environment. These functions range from such basic ones as detection of changes that may lead to tissue damage and eventual destruction of the organism and the implementation of avoidance reactions, to more elaborate representations of the external world implying recognition of shapes, sounds and textures as the basis of planned action or even reflection. Some of these functions confer a clear survival advantage to the organism (prey or mate recognition, escape reactions, etc. ). Others can be considered as an essential part of cognitive processes that contribute, to varying degrees, to the development of individuality and self-consciousness. How can we hope to understand the complexity inherent in this range of functionalities? One of the distinguishing features of the last two decades has been the availability of computational power that has impacted many areas of science. In neurophysiology, computation is used for experiment control, data analysis and for the construction of models that simulate particular systems. Analysis of the behavior of neuronal networks has transcended the limits of neuroscience and is now a discipline in itself, with potential applications both in the neural sciences and in computing sciences.
Download or read book Adaptive Control written by Dianwei Qian and published by . This book was released on 2018-03 with total page 233 pages. Available in PDF, EPUB and Kindle. Book excerpt: Adaptive control is the control method used by a controller which must adapt to a controlled system with parameters which vary, or are initially uncertain. An adaptive control system utilizes on-line identification of which either system parameter or controller parameter, which does not need a priori information about the bounds on these uncertain or time-varying parameters. These approaches consider their control design in the sense of Lyapunov. Besides, there are still some branches by combining adaptive control and other control methods, i.e., nonlinear control methods, intelligent control methods, and predict control methods, to name but a few. Addresses some original contributions reporting the latest advances in adaptive control. It aims to gather the latest research on state-of-the-art methods, applications and research for the adaptive control theory, and recent new findings obtained by the technique of adaptive control. Apparently, the book cannot include all research topics. Different aspects of adaptive control are explored. Chapters includes some new tendencies and developments in research on a adaptive formation controller for multi-robot systems; L1 adaptive control design of the the longitudinal dynamics of a hypersonic vehicle model; adaptive high-gain control of biologically inspired receptor systems; adaptive residual vibration suppression of sigid-flexible coupled systems; neuro-hierarchical sliding mode control for under-actuated mechanical systems; neural network adaptive PID control design based on PLC for a water-level system; and fuzzy-based design of networked control systems with random time delays and packet dropout in the forward communication channel--
Book Synopsis Neural Network Control of Nonlinear Discrete-Time Systems by : Jagannathan Sarangapani
Download or read book Neural Network Control of Nonlinear Discrete-Time Systems written by Jagannathan Sarangapani and published by CRC Press. This book was released on 2018-10-03 with total page 624 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Handbook of Intelligent Control by : David A. White
Download or read book Handbook of Intelligent Control written by David A. White and published by Van Nostrand Reinhold Company. This book was released on 1992 with total page 600 pages. Available in PDF, EPUB and Kindle. Book excerpt: This handbook shows the reader how to develop neural networks and apply them to various engineering control problems. Based on a workshop on aerospace applications, this tutorial covers integration of neural networks with existing control architectures as well as new neurocontrol architectures in nonlinear control.
