Download or read book Adaptive Control of Nonlinear Dynamics Systems Based on RBF Network written by Ho Dac Loc and published by . This book was released on 2005 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In this work, we developed an adaptive fuzzy controller which : 1) does not require an accurate mathematical model of plant under control; 2) uses the error vector as controller's input, therefore it does not require all that components of state vector to be measurable; and 3) guarantees the global stability of the close-loop system. The simulation results show that the adaptive fuzzy controller could successful control the unknown nonlinear dynamic system.

Download or read book Stable Adaptive Control of Unknown Nonlinear Dynamic Systems Using Neural Networks written by Olawale Adetona and published by . This book was released on 1998 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Fully Tuned Radial Basis Function Neural Networks for Flight Control written by N. Sundararajan and published by Springer Science & Business Media. This book was released on 2013-03-09 with total page 167 pages. Available in PDF, EPUB and Kindle. Book excerpt: Fully Tuned Radial Basis Function Neural Networks for Flight Control presents the use of the Radial Basis Function (RBF) neural networks for adaptive control of nonlinear systems with emphasis on flight control applications. A Lyapunov synthesis approach is used to derive the tuning rules for the RBF controller parameters in order to guarantee the stability of the closed loop system. Unlike previous methods that tune only the weights of the RBF network, this book presents the derivation of the tuning law for tuning the centers, widths, and weights of the RBF network, and compares the results with existing algorithms. It also includes a detailed review of system identification, including indirect and direct adaptive control of nonlinear systems using neural networks. Fully Tuned Radial Basis Function Neural Networks for Flight Control is an excellent resource for professionals using neural adaptive controllers for flight control applications.

Download or read book Control and Dynamic Systems written by Cornelius T. Leondes and published by . This book was released on 1998 with total page 438 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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. Key Features 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

Download or read book Neural Network Systems Techniques and Applications written by and published by Academic Press. This book was released on 1998-02-09 with total page 459 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Adaptive Control of Nonlinear Dynamical Systems by Artificial Neural Networks written by Siddik Murat Yesiloglu and published by . This book was released on 1993 with total page 140 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Cutting Edge Robotics written by and published by I-Tech. This book was released on with total page 796 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Functional Adaptive Control written by Simon G. Fabri and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 275 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Adaptive Control of Nonlinear Dynamic System Using Fully Tuned Radial Basis Function Neural Networks written by Yan Li and published by . This book was released on 2001 with total page 196 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Adaptive Systems in Control and Signal Processing 1995 written by Cs. Banyasz and published by Elsevier. This book was released on 2014-05-23 with total page 491 pages. Available in PDF, EPUB and Kindle. Book excerpt: Leading academic and industrial researchers working with adaptive systems and signal processing have been given the opportunity to exchange ideas, concepts and solutions at the IFAC Symposia on Adaptive Systems in Control and Signal Processing. This postprint volume contains all those papers which were presented at the 5th IFAC Symposium in Budapest in 1995. The technical program was composed of a number of invited and contributed sessions and a special case study session, providing a good balance between applications and theory oriented papers.

Download or read book Nonlinear Identification and Control written by G.P. Liu and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 224 pages. Available in PDF, EPUB and Kindle. Book excerpt: The purpose of this monograph is to give the broad aspects of nonlinear identification and control using neural networks. It uses a number of simulated and industrial examples throughout, to demonstrate the operation of nonlinear identification and control techniques using neural networks.

Download or read book Neural Network Engineering in Dynamic Control Systems written by Kenneth J. Hunt and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 285 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 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.

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.

Download or read book Neural Network Based Adaptive Control of Uncertain and Unknown Nonlinear Systems written by and published by . This book was released on 2004 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The objectives of this research effort were to exploit recent advances in neural network (NN) based adaptive control, with the goal of being able to treat a very general class of nonlinear system, for which the dynamics are not only uncertain, but may in fact be unknown except for minimal structural information, such as the relative degree of the regulated output variables. We were particularly interested in designing adaptive control systems that are robust with respect to both parametric uncertainty and unmodeled dynamics. Extensions to decentralized control were also of interest. In addition, we placed a high priority on transition opportunities in aircraft flight control, control of flows, control of flexible space structures, and control of aeroelastic wings.

Download or read book Stable Adaptive Control and Estimation for Nonlinear Systems written by Jeffrey T. Spooner and published by Wiley-Interscience. This book was released on 2004-04-07 with total page 568 pages. Available in PDF, EPUB and Kindle. Book excerpt: Includes a solution manual for problems. Provides MATLAB code for examples and solutions. Deals with robust systems in both theory and practice.

Download or read book Neural Information Processing written by Jun Wang and published by Springer. This book was released on 2006-10-03 with total page 1248 pages. Available in PDF, EPUB and Kindle. Book excerpt: The three volume set LNCS 4232, LNCS 4233, and LNCS 4234 constitutes the refereed proceedings of the 13th International Conference on Neural Information Processing, ICONIP 2006, held in Hong Kong, China in October 2006. The 386 revised full papers presented were carefully reviewed and selected from 1175 submissions.

Download or read book Neural Network Based Adaptive Control for Nonlinear Dynamic Regimes written by Yoonghyun Shin and published by . This book was released on 2005 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Adaptive control designs using neural networks (NNs) based on dynamic inversion are investigated for aerospace vehicles which are operated at highly nonlinear dynamic regimes. NNs play a key role as the principal element of adaptation to approximately cancel the effect of inversion error, which subsequently improves robustness to parametric uncertainty and unmodeled dynamics in nonlinear regimes. An adaptive control scheme previously named composite model reference adaptive control is further developed so that it can be applied to multi-input multi-output output feedback dynamic inversion. It can have adaptive elements in both the dynamic compensator (linear controller) part and/or in the conventional adaptive controller part, also utilizing state estimation information for NN adaptation. This methodology has more flexibility and thus hopefully greater potential than conventional adaptive designs for adaptive flight control in highly nonlinear flight regimes. The stability of the control system is proved through Lyapunov theorems, and validated with simulations. The control designs in this thesis also include the use of pseudo-control hedging techniques which are introduced to prevent the NNs from attempting to adapt to various actuation nonlinearities such as actuator position and rate saturations. Control allocation is introduced for the case of redundant control effectors including thrust vectoring nozzles. A thorough comparison study of conventional and NN-based adaptive designs for a system under a limit cycle, wing-rock, is included in this research, and the NN-based adaptive control designs demonstrate their performances for two highly maneuverable aerial vehicles, NASA F-15 ACTIVE and FQM-117B unmanned aerial vehicle (UAV), operated under various nonlinearities and uncertainties.