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 Control Of Nonlinear Discrete Time Systems
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Author : Jagannathan Sarangapani
language : en
Publisher: CRC Press
Release Date : 2018-10-03
Neural Network Control Of Nonlinear Discrete Time Systems written by Jagannathan Sarangapani and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-10-03 with Technology & Engineering categories.
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.
Recent Advances In Control Problems Of Dynamical Systems And Networks
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Author : Ju H. Park
language : en
Publisher: Springer Nature
Release Date : 2020-08-11
Recent Advances In Control Problems Of Dynamical Systems And Networks written by Ju H. Park and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-08-11 with Technology & Engineering categories.
This edited book introduces readers to new analytical techniques and controller design schemes used to solve the emerging “hottest” problems in dynamic control systems and networks. In recent years, the study of dynamic systems and networks has faced major changes and challenges with the rapid advancement of IT technology, accompanied by the 4th Industrial Revolution. Many new factors that now have to be considered, and which haven’t been addressed from control engineering perspectives to date, are naturally emerging as the systems become more complex and networked. The general scope of this book includes the modeling of the system itself and uncertainty elements, examining stability under various criteria, and controller design techniques to achieve specific control objectives in various dynamic systems and networks. In terms of traditional stability matters, this includes the following special issues: finite-time stability and stabilization, consensus/synchronization, fault-tolerant control, event-triggered control, and sampled-data control for classical linear/nonlinear systems, interconnected systems, fractional-order systems, switched systems, neural networks, and complex networks. In terms of introducing graduate students and professional researchers studying control engineering and applied mathematics to the latest research trends in the areas mentioned above, this book offers an excellent guide.
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 : 1998-11-30
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 1998-11-30 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.
Recent Advances In Control Problems Of Dynamical Systems And Networks
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Author :
language : en
Publisher:
Release Date : 2021
Recent Advances In Control Problems Of Dynamical Systems And Networks written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with Control theory categories.
This edited book introduces readers to new analytical techniques and controller design schemes used to solve the emerging "hottest" problems in dynamic control systems and networks. In recent years, the study of dynamic systems and networks has faced major changes and challenges with the rapid advancement of IT technology, accompanied by the 4th Industrial Revolution. Many new factors that now have to be considered, and which havent been addressed from control engineering perspectives to date, are naturally emerging as the systems become more complex and networked. The general scope of this book includes the modeling of the system itself and uncertainty elements, examining stability under various criteria, and controller design techniques to achieve specific control objectives in various dynamic systems and networks. In terms of traditional stability matters, this includes the following special issues: finite-time stability and stabilization, consensus/synchronization, fault-tolerant control, event-triggered control, and sampled-data control for classical linear/nonlinear systems, interconnected systems, fractional-order systems, switched systems, neural networks, and complex networks. In terms of introducing graduate students and professional researchers studying control engineering and applied mathematics to the latest research trends in the areas mentioned above, this book offers an excellent guide.
Neural Networks For Control
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Author : W. Thomas Miller
language : en
Publisher: MIT Press
Release Date : 1995
Neural Networks For Control written by W. Thomas Miller and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1995 with Computers categories.
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
Neural Network Modeling And Identification Of Dynamical Systems
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Author : Yuri Tiumentsev
language : en
Publisher: Academic Press
Release Date : 2019-05-17
Neural Network Modeling And Identification Of Dynamical Systems written by Yuri Tiumentsev and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-05-17 with Science categories.
Neural Network Modeling and Identification of Dynamical Systems presents a new approach on how to obtain the adaptive neural network models for complex systems that are typically found in real-world applications. The book introduces the theoretical knowledge available for the modeled system into the purely empirical black box model, thereby converting the model to the gray box category. This approach significantly reduces the dimension of the resulting model and the required size of the training set. This book offers solutions for identifying controlled dynamical systems, as well as identifying characteristics of such systems, in particular, the aerodynamic characteristics of aircraft. Covers both types of dynamic neural networks (black box and gray box) including their structure, synthesis and training Offers application examples of dynamic neural network technologies, primarily related to aircraft Provides an overview of recent achievements and future needs in this area
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.
Applications Of Neural Adaptive Control Technology
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Author : Andrzej Dzielinski
language : en
Publisher: World Scientific
Release Date : 1997-09-02
Applications Of Neural Adaptive Control Technology written by Andrzej Dzielinski and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 1997-09-02 with Computers 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.
Neural Network Systems Techniques And Applications
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Author :
language : en
Publisher: Academic Press
Release Date : 1998-02-09
Neural Network Systems Techniques And Applications written by and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998-02-09 with Computers categories.
The book emphasizes neural network structures for achieving practical and effective systems, and provides many examples. Practitioners, researchers, and students in industrial, manufacturing, electrical, mechanical,and production engineering will find this volume a unique and comprehensive reference source for diverse application methodologies. Control and Dynamic Systems covers the important topics of highly effective Orthogonal Activation Function Based Neural Network System Architecture, multi-layer recurrent neural networks for synthesizing and implementing real-time linear control,adaptive control of unknown nonlinear dynamical systems, Optimal Tracking Neural Controller techniques, a consideration of unified approximation theory and applications, techniques for the determination of multi-variable nonlinear model structures for dynamic systems with a detailed treatment of relevant system model input determination, High Order Neural Networks and Recurrent High Order Neural Networks, High Order Moment Neural Array Systems, Online Learning Neural Network controllers, and Radial Bias Function techniques. Coverage includes: Orthogonal Activation Function Based Neural Network System Architecture (OAFNN) Multilayer recurrent neural networks for synthesizing and implementing real-time linear control Adaptive control of unknown nonlinear dynamical systems Optimal Tracking Neural Controller techniques Consideration of unified approximation theory and applications Techniques for determining multivariable nonlinear model structures for dynamic systems, with a detailed treatment of relevant system model input determination