Download or read book Adaptive Neural Network Control of Aircraft written by Robert Richard Smith and published by . This book was released on 1992 with total page 158 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 Application of neural network to adaptive control theory for super augmented aircraft AD A246596 written by Dennis J. Bertrand and published by . This book was released on 1991 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Application of Neural Network to Adaptive Control Theory for Super augmented Aircraft written by Denis J. S. R. Bertrand and published by . This book was released on 1991 with total page 186 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Neural Network Based Adaptive Control of Uncertain and Unknown Nonlinear Systems written by Anthony Calise and published by . This book was released on 2001 with total page 16 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Application of Neural Network to Adaptive Control Theory for Super augmented Aircraft written by Denis J. S. R. Bertrand and published by . This book was released on 1991 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Adaptive Optimization of Aircraft Engine Performance Using Neural Networks written by and published by . This book was released on 1995 with total page 18 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Direct Adaptive Aircraft Control Using Dynamic Cell Structure Neural Networks written by National Aeronautics and Space Administration (NASA) and published by Createspace Independent Publishing Platform. This book was released on 2018-08-17 with total page 36 pages. Available in PDF, EPUB and Kindle. Book excerpt: A Dynamic Cell Structure (DCS) Neural Network was developed which learns topology representing networks (TRNS) of F-15 aircraft aerodynamic stability and control derivatives. The network is integrated into a direct adaptive tracking controller. The combination produces a robust adaptive architecture capable of handling multiple accident and off- nominal flight scenarios. This paper describes the DCS network and modifications to the parameter estimation procedure. The work represents one step towards an integrated real-time reconfiguration control architecture for rapid prototyping of new aircraft designs. Performance was evaluated using three off-line benchmarks and on-line nonlinear Virtual Reality simulation. Flight control was evaluated under scenarios including differential stabilator lock, soft sensor failure, control and stability derivative variations, and air turbulence. Jorgensen, Charles C. Ames Research Center NASA-TM-112198, A-976719A, NAS 1.15:112198 RTOP 519-30-12...

Download or read book Direct Adaptive Aircraft Control Using Dynamic Cell Structure Neural Networks written by and published by . This book was released on 1997 with total page 24 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Model Reference Adaptive Control written by Nhan T. Nguyen and published by Springer. This book was released on 2018-03-01 with total page 453 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook provides readers with a good working knowledge of adaptive control theory through applications. It is intended for students beginning masters or doctoral courses, and control practitioners wishing to get up to speed in the subject expeditiously. Readers are taught a wide variety of adaptive control techniques starting with simple methods and extending step-by-step to more complex ones. Stability proofs are provided for all adaptive control techniques without obfuscating reader understanding with excessive mathematics. The book begins with standard model-reference adaptive control (MRAC) for first-order, second-order, and multi-input, multi-output systems. Treatment of least-squares parameter estimation and its extension to MRAC follow, helping readers to gain a different perspective on MRAC. Function approximation with orthogonal polynomials and neural networks, and MRAC using neural networks are also covered. Robustness issues connected with MRAC are discussed, helping the student to appreciate potential pitfalls of the technique. This appreciation is encouraged by drawing parallels between various aspects of robustness and linear time-invariant systems wherever relevant. Following on from the robustness problems is material covering robust adaptive control including standard methods and detailed exposition of recent advances, in particular, the author’s work on optimal control modification. Interesting properties of the new method are illustrated in the design of adaptive systems to meet stability margins. This method has been successfully flight-tested on research aircraft, one of various flight-control applications detailed towards the end of the book along with a hybrid adaptive flight control architecture that combines direct MRAC with least-squares indirect adaptive control. In addition to the applications, understanding is encouraged by the use of end-of-chapter exercises and associated MATLAB® files. Readers will need no more than the standard mathematics for basic control theory such as differential equations and matrix algebra; the book covers the foundations of MRAC and the necessary mathematical preliminaries.

Download or read book Adaptive Neural Network Inverse Controller for General Aviation Safety written by Urpo J. Pesonen and published by . This book was released on 2003 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

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 190 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

Download or read book Applications of Neural Networks in High Assurance Systems written by Johann M.Ph. Schumann and published by Springer Science & Business Media. This book was released on 2010-02-28 with total page 255 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Applications of Neural Networks in High Assurance Systems" is the first book directly addressing a key part of neural network technology: methods used to pass the tough verification and validation (V&V) standards required in many safety-critical applications. The book presents what kinds of evaluation methods have been developed across many sectors, and how to pass the tests. A new adaptive structure of V&V is developed in this book, different from the simple six sigma methods usually used for large-scale systems and different from the theorem-based approach used for simplified component subsystems.

Download or read book Applications of Neural Networks to Adaptive Control written by Russell W Scott (II.) and published by . This book was released on 1989 with total page 117 pages. Available in PDF, EPUB and Kindle. Book excerpt: The amount of a priori knowledge required to design some modern control systems is becoming prohibitive. Two current methods addressing this problem are robust control, in which the control design is insensitive to errors in system knowledge, and adaptive control, in which the control law is adjusted in response to a continually updated model of the system. This thesis examines the application of parallel distributed processing (neural networks) to the problem of adaptive control. The structure of neural networks is introduced, focusing on the Backpropagation paradigm. A general form of controller consistent with use in neural networks is developed and combined with a discussion of linear east squares parameter estimation techniques to suggest a structure for neural network adaptive controllers. This neural network adaptive control structure is then applied to a number of estimation and control problems using as a model the longitudinal motion of the A-4 aircraft. The purpose of this thesis is to develop and demonstrate a neural network adaptive control structure consistent with adaptive control theory. Theses. (RH).

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 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

Download or read book Methods and Procedures for the Verification and Validation of Artificial Neural Networks written by Brian J. Taylor and published by Springer Science & Business Media. This book was released on 2006-03-20 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural networks are members of a class of software that have the potential to enable intelligent computational systems capable of simulating characteristics of biological thinking and learning. Currently no standards exist to verify and validate neural network-based systems. NASA Independent Verification and Validation Facility has contracted the Institute for Scientific Research, Inc. to perform research on this topic and develop a comprehensive guide to performing V&V on adaptive systems, with emphasis on neural networks used in safety-critical or mission-critical applications. Methods and Procedures for the Verification and Validation of Artificial Neural Networks is the culmination of the first steps in that research. This volume introduces some of the more promising methods and techniques used for the verification and validation (V&V) of neural networks and adaptive systems. A comprehensive guide to performing V&V on neural network systems, aligned with the IEEE Standard for Software Verification and Validation, will follow this book.