Neural Adaptive Control Technology PDF Download
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| Author | : Rafa? ?bikowski |
| Publisher | : World Scientific |
| Total Pages | : 368 |
| Release | : 1996 |
| Genre | : Technology & Engineering |
| ISBN | : 9789810225575 |
Download Neural Adaptive Control Technology Book in PDF, ePub and Kindle
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.
| Author | : Andrzej Dzielinski |
| Publisher | : World Scientific |
| Total Pages | : 318 |
| Release | : 1997-09-02 |
| Genre | : Computers |
| ISBN | : 9814497339 |
Download Applications Of Neural Adaptive Control Technology Book in PDF, ePub and Kindle
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.
| Author | : George A. Rovithakis |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 203 |
| Release | : 2012-12-06 |
| Genre | : Computers |
| ISBN | : 1447107853 |
Download Adaptive Control with Recurrent High-order Neural Networks Book in PDF, ePub and Kindle
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.
| Author | : Rafał Żbikowski |
| Publisher | : |
| Total Pages | : 347 |
| Release | : 1996 |
| Genre | : Adaptive control systems |
| ISBN | : 9786613948335 |
Download Neural Adaptive Control Technology Book in PDF, ePub and Kindle
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.
| Author | : Yang Li |
| Publisher | : Academic Press |
| Total Pages | : 186 |
| Release | : 2018-11-16 |
| Genre | : Technology & Engineering |
| ISBN | : 0128154322 |
Download Adaptive Sliding Mode Neural Network Control for Nonlinear Systems Book in PDF, ePub and Kindle
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
| Author | : Jens Kalkkuhl |
| Publisher | : World Scientific |
| Total Pages | : 328 |
| Release | : 1997 |
| Genre | : Technology & Engineering |
| ISBN | : 9789810231514 |
Download Applications of Neural Adaptive Control Technology Book in PDF, ePub and Kindle
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.
| Author | : Omid Omidvar |
| Publisher | : Elsevier |
| Total Pages | : 375 |
| Release | : 1997-02-24 |
| Genre | : Computers |
| ISBN | : 0080537391 |
Download Neural Systems for Control Book in PDF, ePub and Kindle
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
| Author | : Ali Ismael |
| Publisher | : |
| Total Pages | : 402 |
| Release | : 1998 |
| Genre | : Adaptive control systems |
| ISBN | : |
Download Neural Adaptive Control Systems Book in PDF, ePub and Kindle
The main research contribution in this dissertation is the development of a new neural adaptive control system design methodology. In this methodology, a neural network architectures, the columnar fuzzy neural network architecture (CFNA) based on distributed representations, has been developed. The columnar fuzzy neural network architecture (CFNA) can learn approximations of nonlinear, multivariable mappings. The CFNA is used to implement plant identification and the controller subsystems of neural model reference adaptive control systems. The adaptive performance of the CFNA is due to its on-line learning capability. An extended version of the error back propagation learning algorithm is developed for this purpose. The extended error back propagation algorithm utilizes a quadratic measure of output error. The new design methodology provides stability in the Liapunov sense through a constraint mechanism imposed on the learning algorithm in the weight space of the CFNA. Knowledge acquired from expert human operators of complex nonlinear plants in the form of fuzzy rules is mapped into an initial set of weights for the CFNA controller. The learning ability of the CFNA controller underlies its potential capabilities to eliminate erroneous rules, learn necessary new rules, and slightly modify or fine-tune existing rules.
| Author | : Shubo Wang |
| Publisher | : Elsevier |
| Total Pages | : 304 |
| Release | : 2024-02-01 |
| Genre | : Technology & Engineering |
| ISBN | : 0443155755 |
Download Parameter Estimation and Adaptive Control for Nonlinear Servo Systems Book in PDF, ePub and Kindle
Parameter Estimation and Adaptive Control for Nonlinear Servo Systems presents the latest advances in observer-based control design, focusing on adaptive control for nonlinear systems such as adaptive neural network control, adaptive parameter estimation, and system identification. This book offers an array of new real-world applications in the field. Written by eminent scientists in the field of control theory, this book covers the latest advances in observer-based control design. It provides fundamentals, algorithms, and it discusses key applications in the fields of power systems, robotics and mechatronics, flight and automotive systems. Presents a clear and concise introduction to the latest advances in parameter estimation and adaptive control with several concise applications for servo systems Covers a wide range of applications usually not found in similar books, such as power systems, robotics, mechatronics, aeronautics, and industrial systems Contains worked examples which make it ideal for advanced courses as well as for researchers starting to work in the field, particularly suitable for engineers wishing to enter the field quickly and efficiently
| Author | : Sam Shuzhi Ge |
| Publisher | : World Scientific |
| Total Pages | : 397 |
| Release | : 1998-12-04 |
| Genre | : Technology & Engineering |
| ISBN | : 9814496227 |
Download Adaptive Neural Network Control Of Robotic Manipulators Book in PDF, ePub and Kindle
Recently, there has been considerable research interest in neural network control of robots, and satisfactory results have been obtained in solving some of the special issues associated with the problems of robot control in an “on-and-off” fashion. This book is dedicated to issues on adaptive control of robots based on neural networks. The text has been carefully tailored to (i) give a comprehensive study of robot dynamics, (ii) present structured network models for robots, and (iii) provide systematic approaches for neural network based adaptive controller design for rigid robots, flexible joint robots, and robots in constraint motion. Rigorous proof of the stability properties of adaptive neural network controllers is provided. Simulation examples are also presented to verify the effectiveness of the controllers, and practical implementation issues associated with the controllers are also discussed.
