Self Organizing Radial Basis Function Networks For Adaptive Flight Control And Aircraft Engine State Estimation
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Self Organizing Radial Basis Function Networks For Adaptive Flight Control And Aircraft Engine State Estimation
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Author : Praveen Shankar
language : en
Publisher:
Release Date : 2007
Self Organizing Radial Basis Function Networks For Adaptive Flight Control And Aircraft Engine State Estimation written by Praveen Shankar and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with Adaptive control systems categories.
Abstract: The performance of nonlinear control algorithms such as feedback linearization and dynamic inversion is heavily dependent on the fidelity of the dynamic model being inverted. Incomplete or incorrect knowledge of the dynamics results in reduced performance and may lead to instability. Augmenting the baseline controller with approximators which utilize a parametrization structure that is adapted online reduces the effect of this error between the design model and actual dynamics. However, currently existing parameterizations employ a fixed set of basis functions that do not guarantee arbitrary tracking error performance. To address this problem, we develop a self-organizing parametrization structure that is proven to be stable and can guarantee arbitrary tracking error performance. The training algorithm to grow the network and adapt the parameters is derived from Lyapunov theory. In addition to growing the network of basis functions, a pruning strategy is incorporated to keep the size of the network as small as possible. This algorithm is implemented on a high performance flight vehicle such as F-15 military aircraft. The baseline dynamic inversion controller is augmented with a Self-Organizing Radial Basis Function Network (SORBFN) to minimize the effect of the inversion error which may occur due to imperfect modeling, approximate inversion or sudden changes in aircraft dynamics. The dynamic inversion controller is simulated for different situations including control surface failures, modeling errors and external disturbances with and without the adaptive network. A performance measure of maximum tracking error is specified for both the controllers a priori. Excellent tracking error minimization to a pre-specified level using the adaptive approximation based controller was achieved while the baseline dynamic inversion controller failed to meet this performance specification. The performance of the SORBFN based controller is also compared to a fixed RBF network based adaptive controller. While the fixed RBF network based controller which is tuned to compensate for control surface failures fails to achieve the same performance under modeling uncertainty and disturbances, the SORBFN is able to achieve good tracking convergence under all error conditions.
Dissertation Abstracts International
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Author :
language : en
Publisher:
Release Date : 2008
Dissertation Abstracts International written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with Dissertations, Academic categories.
Scientific And Technical Aerospace Reports
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Author :
language : en
Publisher:
Release Date : 1995
Scientific And Technical Aerospace Reports written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1995 with Aeronautics categories.
International Aerospace Abstracts
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Author :
language : en
Publisher:
Release Date : 1998
International Aerospace Abstracts written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998 with Aeronautics categories.
Neural Networks For Pattern Recognition
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Author : Christopher M. Bishop
language : en
Publisher: Oxford University Press
Release Date : 1995-11-23
Neural Networks For Pattern Recognition written by Christopher M. Bishop and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1995-11-23 with Computers categories.
Statistical pattern recognition; Probability density estimation; Single-layer networks; The multi-layer perceptron; Radial basis functions; Error functions; Parameter optimization algorithms; Pre-processing and feature extraction; Learning and generalization; Bayesian techniques; Appendix; References; Index.
Deterministic Artificial Intelligence
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Author : Timothy Sands
language : en
Publisher: BoD – Books on Demand
Release Date : 2020-05-27
Deterministic Artificial Intelligence written by Timothy Sands and has been published by BoD – Books on Demand this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-05-27 with Computers categories.
Kirchhoff’s laws give a mathematical description of electromechanics. Similarly, translational motion mechanics obey Newton’s laws, while rotational motion mechanics comply with Euler’s moment equations, a set of three nonlinear, coupled differential equations. Nonlinearities complicate the mathematical treatment of the seemingly simple action of rotating, and these complications lead to a robust lineage of research culminating here with a text on the ability to make rigid bodies in rotation become self-aware, and even learn. This book is meant for basic scientifically inclined readers commencing with a first chapter on the basics of stochastic artificial intelligence to bridge readers to very advanced topics of deterministic artificial intelligence, espoused in the book with applications to both electromechanics (e.g. the forced van der Pol equation) and also motion mechanics (i.e. Euler’s moment equations). The reader will learn how to bestow self-awareness and express optimal learning methods for the self-aware object (e.g. robot) that require no tuning and no interaction with humans for autonomous operation. The topics learned from reading this text will prepare students and faculty to investigate interesting problems of mechanics. It is the fondest hope of the editor and authors that readers enjoy the book.
Reinforcement Learning Second Edition
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Author : Richard S. Sutton
language : en
Publisher: MIT Press
Release Date : 2018-11-13
Reinforcement Learning Second Edition written by Richard S. Sutton and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-11-13 with Computers categories.
The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.
Feedback Systems
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Author : Karl Johan Åström
language : en
Publisher: Princeton University Press
Release Date : 2021-02-02
Feedback Systems written by Karl Johan Åström and has been published by Princeton University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-02 with Technology & Engineering categories.
The essential introduction to the principles and applications of feedback systems—now fully revised and expanded This textbook covers the mathematics needed to model, analyze, and design feedback systems. Now more user-friendly than ever, this revised and expanded edition of Feedback Systems is a one-volume resource for students and researchers in mathematics and engineering. It has applications across a range of disciplines that utilize feedback in physical, biological, information, and economic systems. Karl Åström and Richard Murray use techniques from physics, computer science, and operations research to introduce control-oriented modeling. They begin with state space tools for analysis and design, including stability of solutions, Lyapunov functions, reachability, state feedback observability, and estimators. The matrix exponential plays a central role in the analysis of linear control systems, allowing a concise development of many of the key concepts for this class of models. Åström and Murray then develop and explain tools in the frequency domain, including transfer functions, Nyquist analysis, PID control, frequency domain design, and robustness. Features a new chapter on design principles and tools, illustrating the types of problems that can be solved using feedback Includes a new chapter on fundamental limits and new material on the Routh-Hurwitz criterion and root locus plots Provides exercises at the end of every chapter Comes with an electronic solutions manual An ideal textbook for undergraduate and graduate students Indispensable for researchers seeking a self-contained resource on control theory
Identification Modeling And Characteristics Of Miniature Rotorcraft
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Author : Bernard Mettler
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-03-14
Identification Modeling And Characteristics Of Miniature Rotorcraft written by Bernard Mettler 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 2013-03-14 with Technology & Engineering categories.
Identification Modeling and Characteristics of Miniature Rotorcraft introduces an approach to developing a simple and effective linear parameterized model of vehicle dynamics using the CIFERâ identification tool created by the Army/NASA Rotorcraft Division. It also presents the first application of the advanced control system optimization tool CONDUITâ to systematically and efficiently tune control laws for a model-scale UAV helicopter against multiple and competing dynamic response criteria. Identification Modeling and Characteristics of Miniature Rotorcraft presents the detailed account of how the theory was developed, the experimentation performed, and how the results were used. This book will serve as a basic and illustrative guide for all students that are interested in developing autonomous flying helicopters.
Neural Networks For Modelling And Control Of Dynamic Systems
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Author : M. Norgaard
language : en
Publisher:
Release Date : 2003
Neural Networks For Modelling And Control Of Dynamic Systems written by M. Norgaard and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003 with categories.