Stable Adaptive Neural Network Control

This book is motivated by the need for systematic design approaches for stable adaptive control using approximation-based techniques.

Stable Adaptive Neural Network Control

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.

Stable Adaptive Control and Estimation for Nonlinear Systems

The text presents a control methodology that may be verified with mathematical rigor while possessing the flexibility and ease of implementation associated with "intelligent control" approaches.

Stable Adaptive Control and Estimation for Nonlinear Systems

Includes a solution manual for problems. Provides MATLAB code for examples and solutions. Deals with robust systems in both theory and practice.

Radial Basis Function RBF Neural Network Control for Mechanical Systems

In this book, a broad range of implementable neural network control design methods for mechanical systems are presented, such as robot manipulators, inverted pendulums, single link flexible joint robots, motors, etc.

Radial Basis Function  RBF  Neural Network Control for Mechanical Systems

Radial Basis Function (RBF) Neural Network Control for Mechanical Systems is motivated by the need for systematic design approaches to stable adaptive control system design using neural network approximation-based techniques. The main objectives of the book are to introduce the concrete design methods and MATLAB simulation of stable adaptive RBF neural control strategies. In this book, a broad range of implementable neural network control design methods for mechanical systems are presented, such as robot manipulators, inverted pendulums, single link flexible joint robots, motors, etc. Advanced neural network controller design methods and their stability analysis are explored. The book provides readers with the fundamentals of neural network control system design. This book is intended for the researchers in the fields of neural adaptive control, mechanical systems, Matlab simulation, engineering design, robotics and automation. Jinkun Liu is a professor at Beijing University of Aeronautics and Astronautics.

Advances in Neural Networks ISNN 2007

28. 8. Polycarpou, M. M.: Stable Adaptive Neural Control Scheme for Nonlinear
Systems. IEEE Trans. Autom. Control 41 (3) (1996) 447–451 9. Gao, W., Selmic,
Rastko, R.: Neural Network Control of a Class of Nonlinear Systems with Actuator
 ...

Advances in Neural Networks   ISNN 2007

This book is part of a three volume set that constitutes the refereed proceedings of the 4th International Symposium on Neural Networks, ISNN 2007, held in Nanjing, China in June 2007. Coverage includes neural networks for control applications, robotics, data mining and feature extraction, chaos and synchronization, support vector machines, fault diagnosis/detection, image/video processing, and applications of neural networks.

Neural Network Control of Nonlinear Discrete Time Systems

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 ...

Neural Network Control of Nonlinear Discrete Time Systems

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.

Adaptive Neural Network Control of Robotic Manipulators

This book is dedicated to issues on adaptive control of robots based on neural networks.

Adaptive Neural Network Control of Robotic Manipulators

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.

Advances in Natural Computation

Research on a Direct Adaptive Neural Network Control Method of Nonlinear
Systems Weijin Jiang1, Yusheng Xu2, and ... function of the sum of residual and
approximation error, the closed-loop control system is shown to be globally
stable, ...

Advances in Natural Computation

The three volume set LNCS 3610, LNCS 3611, and LNCS 3612 constitutes the refereed proceedings of the First International Conference on Natural Computation, ICNC 2005, held in Changsha, China, in August 2005 as a joint event in federation with the Second International Conference on Fuzzy Systems and Knowledge Discovery FSKD 2005 (LNAI volumes 3613 and 3614).The program committee selected 313 carefully revised full papers and 189 short papers for presentation in three volumes from 1887 submissions. The first volume includes all the contributions related to learning algorithms and architectures in neural networks, neurodynamics, statistical neural network models and support vector machines, and other topics in neural network models; cognitive science, neuroscience informatics, bioinformatics, and bio-medical engineering, and neural network applications as communications and computer networks, expert system and informatics, and financial engineering. The second volume concentrates on neural network applications such as pattern recognition and diagnostics, robotics and intelligent control, signal processing and multi-media, and other neural network applications; evolutionary learning, artificial immune systems, evolutionary theory, membrane, molecular, DNA computing, and ant colony systems. The third volume deals with evolutionary methodology, quantum computing, swarm intelligence and intelligent agents; natural computation applications as bioinformatics and bio-medical engineering, robotics and intelligent control, and other applications of natural computation; hardware implementations of natural computation, and fuzzy neural systems as well as soft computing.

Applications of Neural Adaptive Control Technology

The network is utilised in stable adaptive tracking control designs employing a
parallel combination of a neural network , a PDcontroller and a sliding - mode
component . 1 The Gaussian basis functions are used to determine the required ...

Applications of Neural Adaptive Control Technology

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.

Functional Adaptive Control

Unique in its systematic approach to stochastic systems, this book presents a wide range of techniques that lead to novel strategies for effecting intelligent control of complex systems that are typically characterised by uncertainty, ...

Functional Adaptive Control

Unique in its systematic approach to stochastic systems, this book presents a wide range of techniques that lead to novel strategies for effecting intelligent control of complex systems that are typically characterised by uncertainty, nonlinear dynamics, component failure, unpredictable disturbances, multi-modality and high dimensional spaces.

Mechatronic Systems 2004

A perceptron network for functional identification and control of nonlinear systems
. IEEE Trans . Neural Networks , Vol . 4 , No. 6 pp . 982–988 . Narendra , K.S. and
A.M. Annaswamy ( 1989 ) . Stable Adaptive Systems . Prentice - Hall .

Mechatronic Systems 2004


Robust Adaptive Control for Fractional Order Systems with Disturbance and Saturation

This book offers chapter coverage of fractional calculus and fractional-order systems; fractional-order PID controller and fractional-order disturbance observer; design of fractional-order controllers for nonlinear chaotic systems and some ...

Robust Adaptive Control for Fractional Order Systems with Disturbance and Saturation

A treatise on investigating tracking control and synchronization control of fractional-order nonlinear systems with system uncertainties, external disturbance, and input saturation Robust Adaptive Control for Fractional-Order Systems, with Disturbance and Saturation provides the reader with a good understanding on how to achieve tracking control and synchronization control of fractional-order nonlinear systems with system uncertainties, external disturbance, and input saturation. Although some texts have touched upon control of fractional-order systems, the issues of input saturation and disturbances have rarely been considered together. This book offers chapter coverage of fractional calculus and fractional-order systems; fractional-order PID controller and fractional-order disturbance observer; design of fractional-order controllers for nonlinear chaotic systems and some applications; sliding mode control for fractional-order nonlinear systems based on disturbance observer; disturbance observer based neural control for an uncertain fractional-order rotational mechanical system; adaptive neural tracking control for uncertain fractional-order chaotic systems subject to input saturation and disturbance; stabilization control of continuous-time fractional positive systems based on disturbance observer; sliding mode synchronization control for fractional-order chaotic systems with disturbance; and more. Based on the approximation ability of the neural network (NN), the adaptive neural control schemes are reported for uncertain fractional-order nonlinear systems Covers the disturbance estimation techniques that have been developed to alleviate the restriction faced by traditional feedforward control and reject the effect of external disturbances for uncertain fractional-order nonlinear systems By combining the NN with the disturbance observer, the disturbance observer based adaptive neural control schemes have been studied for uncertain fractional-order nonlinear systems with unknown disturbances Considers, together, the issue of input saturation and the disturbance for the control of fractional-order nonlinear systems in the present of system uncertainty, external disturbance, and input saturation Robust Adaptive Control for Fractional-Order Systems, with Disturbance and Saturation can be used as a reference for the academic research on fractional-order nonlinear systems or used in Ph.D. study of control theory and engineering.

Adaptive Neural Control of Walking Robots

Miller III , W . T . ( 1994 ) , Real - Time Neural Network Control of a Biped Walking
Robot , IEEE Control Systems Magazine , February 1994 , pp . 41 - 48 . Narendra
, K . S . , Annaswamy , ( 1989 ) , Stable Adaptive Systems , Prentice Hall , New ...

Adaptive Neural Control of Walking Robots

An important title in the “Engineering Research Series” Adaptive Neural Control of Walking Robots brings together two important areas of technological development: adaptive neural control and mobile/walking robots. The development and management of locomotory robots has not only produced machines that are useful on uneven ground, for climbing walls, in dangerous or hostile environments, and in remote situations, but has also been responsible for some excellent engineering and technological solutions in the field of responsive or adaptive control. Adaptive Neural Control of Walking Robots will be of interest to engineers and research establishments working on the practical or industrial application of climbing and walking robots. The book will also interest those concerned with robot control systems, adaptive neural control, and applications of autonomous or programmable machine behaviour in multijointed systems. COMPLETE CONTENTS: Introduction and background A generic intelligent control hierarchy Insect observations and hexapod design Models for co-ordination of walking Leg trajectory planning and generation Hexapod kinematics and dynamics The theory of stable adaptive neural control for open-chain systems Stable neural control of systems with constraints and closed kinematic chains Hexapod experiments Summary, conclusions, and outlook.

Advances in Neural Networks ISNN

Liu , C . , Chen , F . : Adaptive Control of Nonlinear Continuous Systems Using
Neural Network - General Relative Degree and MIMO Case . ... Polycarpou , M .
M . : Stable Adaptive Neural Control Scheme for Nonlinear Systems . IEEE Trans
.

Advances in Neural Networks  ISNN


1997 IEEE International Symposium on Intelligent Control

Stable Adaptive Control Using Fuzzy Systems and Neural Networks " , IEEE
Trans . on Fuzzy Systems , 4 , no . ... [ 34 ] Liu , C . C . , and F . C . Chen , "
Adaptive Control of Nonlinear Continuous - time systems Using Neural Network -
General ...

1997 IEEE International Symposium on Intelligent Control


Complex Valued Nonlinear Adaptive Filters

Noncircularity, Widely Linear and Neural Models Danilo P. Mandic, Vanessa Su
Lee Goh ... SYSTEMS: Feedforward Neural Network Perspectives Spooner,
Maggiore, Ordóñez, and Passino / STABLE ADAPTIVE CONTROL AND
ESTIMATION ...

Complex Valued Nonlinear Adaptive Filters

This book was written in response to the growing demand for a text that provides a unified treatment of linear and nonlinear complex valued adaptive filters, and methods for the processing of general complex signals (circular and noncircular). It brings together adaptive filtering algorithms for feedforward (transversal) and feedback architectures and the recent developments in the statistics of complex variable, under the powerful frameworks of CR (Wirtinger) calculus and augmented complex statistics. This offers a number of theoretical performance gains, which is illustrated on both stochastic gradient algorithms, such as the augmented complex least mean square (ACLMS), and those based on Kalman filters. This work is supported by a number of simulations using synthetic and real world data, including the noncircular and intermittent radar and wind signals.

Journal of Dynamic Systems Measurement and Control

[ 11 ] Fabri S . , and Kadirkamanathan , V . , 1996 , “ Dynamic Structure Neural
Networks for Stable Adaptive Control of ... Control 46 , pp . 1599 – 1605 . [ 21 ]
Lin , C . - L . , and Lin , T . - Y . , 2002 , “ Approach to Adaptive Neural Net - Based
H ...

Journal of Dynamic Systems  Measurement  and Control


Advances in Neural Networks ISNN 2006

This is Volume I of a three volume set constituting the refereed proceedings of the Third International Symposium on Neural Networks, ISNN 2006. 616 revised papers are organized in topical sections on neurobiological analysis, theoretical ...

Advances in Neural Networks   ISNN 2006

This is Volume I of a three volume set constituting the refereed proceedings of the Third International Symposium on Neural Networks, ISNN 2006. 616 revised papers are organized in topical sections on neurobiological analysis, theoretical analysis, neurodynamic optimization, learning algorithms, model design, kernel methods, data preprocessing, pattern classification, computer vision, image and signal processing, system modeling, robotic systems, transportation systems, communication networks, information security, fault detection, financial analysis, bioinformatics, biomedical and industrial applications, and more.

Neural Network Control

Polycarpou , M . M . , Stable adaptive neural scheme for nonlinear systems , IEEE
Trans . on Neural Networks , vol . 7 , no . 3 , pp . 447 - 451 , 1996 . Polycarpou , M
. M . and P . A . Ioannou , Modeling , identification and stable adaptive control ...

Neural Network Control

"While the book is written to serve as an advanced control reference on NN control for researchers, postgraduates and senior undergraduates, it should be equally useful to those industrial practitioners who are keen to explore the use of advanced neural network control in real problems. The prerequisite for gaining maximum benefit from this book is a basic knowledge of control systems, such as that imparted by a first undergraduate course on control systems engineering."--Jacket.

Mechanical Engineers Handbook Volume 2

R . M . Sanner and J . - J . E . Slotine , “ Gaussian Networks for Direct Adaptive
Control , ” IEEE Transactions on Neural ... C . C . Hang , T . H . Lee , and T .
Zhang , Stable Adaptive Neural Network Control , Kluwer , Boston , MA , 2001 .
21 .

Mechanical Engineers  Handbook  Volume 2

The updated Revision of the Bestseller--In a more Useful Format! Mechanical Engineers' Handbook has a long tradition as a single resource of valuable information related to specialty areas in the diverse industries and job functions in which mechanical engineers work. This Third Edition, the most aggressive revision to date, goes beyond the straight data, formulas, and calculations provided in other handbooks and focuses on authoritative discussions, real-world examples, and insightful analyses while covering more topics that in previous editions. Book 2: Instrumentation, Systems, Controls, and MEMS is comprised of two major parts, conveniently put together because feedback control systems require measurement transducers. The first part covers instrumentation, including transducer design, strain gages, flow meters, digital integrated circuits, and issues involved in processing transducer signals and acquiring and displaying data. The second part addresses systems and control, including: * Control system design, analysis, and performance modification * Design of servoactuators, controllers, and general-purpose control devices * "New departures" in mechanical engineering, including neural networks, mechatronics, and MEMS