Fully Tuned Radial Basis Function Neural Networks for Flight Control
Fully Tuned Radial Basis Function Neural Networks for Flight Control

Author: N. Sundararajan

Publisher: Springer Science & Business Media

Published: 2013-03-09

Total Pages: 167

ISBN-13: 1475752865

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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.

Nonlinear Flight Control Using Adaptive Critic Based Neural Networks
Nonlinear Flight Control Using Adaptive Critic Based Neural Networks

Author: Sergio Esteban Roncero

Publisher:

Published: 2002

Total Pages: 204

ISBN-13:

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"Ultimately the purpose of the nonlinear flight control system developed in this work is to pave the way for an adaptive reconfigurable nonlinear controller that would make aviation a safe way of transportation even in the presence of control failures and/or damaged aerodynamic surfaces."--Abstract, p. iii.

Adaptive Compensation of Nonlinear Actuators for Flight Control Applications
Adaptive Compensation of Nonlinear Actuators for Flight Control Applications

Author: Dipankar Deb

Publisher: Springer Nature

Published: 2021-07-22

Total Pages: 129

ISBN-13: 9811641617

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This book provides a basic understanding of adaptive control and its applications in Flight control. It discusses the designing of an adaptive feedback control system and analyzes this for flight control of linear and nonlinear aircraft models using synthetic jet actuators. It also discusses control methodologies and the application of control techniques which will help practicing flight control and active flow control researchers. It also covers modelling and control designs which will also benefit researchers from the background of fluid mechanics and health management of actuation systems. The unique feature of this book is characterization of synthetic jet actuator nonlinearities over a wide range of angles of attack, an adaptive compensation scheme for such nonlinearities, and a systematic framework for feedback control of aircraft dynamics with synthetic jet actuators.

System Identification and Adaptive Control
System Identification and Adaptive Control

Author: Yiannis Boutalis

Publisher: Springer Science & Business

Published: 2014-04-23

Total Pages: 316

ISBN-13: 3319063642

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Presenting current trends in the development and applications of intelligent systems in engineering, this monograph focuses on recent research results in system identification and control. The recurrent neurofuzzy and the fuzzy cognitive network (FCN) models are presented. Both models are suitable for partially-known or unknown complex time-varying systems. Neurofuzzy Adaptive Control contains rigorous proofs of its statements which result in concrete conclusions for the selection of the design parameters of the algorithms presented. The neurofuzzy model combines concepts from fuzzy systems and recurrent high-order neural networks to produce powerful system approximations that are used for adaptive control. The FCN model stems from fuzzy cognitive maps and uses the notion of “concepts” and their causal relationships to capture the behavior of complex systems. The book shows how, with the benefit of proper training algorithms, these models are potent system emulators suitable for use in engineering systems. All chapters are supported by illustrative simulation experiments, while separate chapters are devoted to the potential industrial applications of each model including projects in: • contemporary power generation; • process control and • conventional benchmarking problems. Researchers and graduate students working in adaptive estimation and intelligent control will find Neurofuzzy Adaptive Control of interest both for the currency of its models and because it demonstrates their relevance for real systems. The monograph also shows industrial engineers how to test intelligent adaptive control easily using proven theoretical results.

Parameter Estimation and Adaptive Control for Nonlinear Servo Systems
Parameter Estimation and Adaptive Control for Nonlinear Servo Systems

Author: Shubo Wang

Publisher: Elsevier

Published: 2024-02-01

Total Pages: 304

ISBN-13: 0443155755

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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

Neural Network Modeling and Identification of Dynamical Systems
Neural Network Modeling and Identification of Dynamical Systems

Author: Yuri Tiumentsev

Publisher: Academic Press

Published: 2019-05-17

Total Pages: 332

ISBN-13: 0128154306

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Neural Network Modeling and Identification of Dynamical Systems presents a new approach on how to obtain the adaptive neural network models for complex systems that are typically found in real-world applications. The book introduces the theoretical knowledge available for the modeled system into the purely empirical black box model, thereby converting the model to the gray box category. This approach significantly reduces the dimension of the resulting model and the required size of the training set. This book offers solutions for identifying controlled dynamical systems, as well as identifying characteristics of such systems, in particular, the aerodynamic characteristics of aircraft. Covers both types of dynamic neural networks (black box and gray box) including their structure, synthesis and training Offers application examples of dynamic neural network technologies, primarily related to aircraft Provides an overview of recent achievements and future needs in this area

Adaptive Approximation Based Control
Adaptive Approximation Based Control

Author: Jay A. Farrell

Publisher: John Wiley & Sons

Published: 2006-04-14

Total Pages: 440

ISBN-13: 0471781800

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A highly accessible and unified approach to the design and analysis of intelligent control systems Adaptive Approximation Based Control is a tool every control designer should have in his or her control toolbox. Mixing approximation theory, parameter estimation, and feedback control, this book presents a unified approach designed to enable readers to apply adaptive approximation based control to existing systems, and, more importantly, to gain enough intuition and understanding to manipulate and combine it with other control tools for applications that have not been encountered before. The authors provide readers with a thought-provoking framework for rigorously considering such questions as: * What properties should the function approximator have? * Are certain families of approximators superior to others? * Can the stability and the convergence of the approximator parameters be guaranteed? * Can control systems be designed to be robust in the face of noise, disturbances, and unmodeled effects? * Can this approach handle significant changes in the dynamics due to such disruptions as system failure? * What types of nonlinear dynamic systems are amenable to this approach? * What are the limitations of adaptive approximation based control? Combining theoretical formulation and design techniques with extensive use of simulation examples, this book is a stimulating text for researchers and graduate students and a valuable resource for practicing engineers.