Flight Control with Adaptive Critic Neural Network
Author: Dongchen Han
Publisher:
Published: 2001
Total Pages: 163
ISBN-13:
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Nonlinear Flight Control Using Adaptive Critic Based Neural Networks
Author: Sergio Esteban Roncero
Publisher:
Published: 2002
Total Pages: 204
ISBN-13:
DOWNLOAD EBOOK"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.
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
DOWNLOAD EBOOKFully 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.
Adaptive Critic Flight Control for a General Aviation Aircraft: Simulations for the Beech Bonanza Fly-by-wire Testbed
Author: Rajeev Chandramohan
Publisher:
Published: 2007
Total Pages: 186
ISBN-13:
DOWNLOAD EBOOKAn adaptive and reconfigurable flight control system is developed for a general aviation aircraft. The flight control system consisting of two neural networks is developed using a two phase procedure called the pre-training phase and the online training phase. The adaptive critic method used in this thesis was developed by Ferrari and Stengel. In the pre-training phase the architecture and initial weights of the neural network are determined based on linear control. A set of local gains for the linearized model of the plant is obtained at different design points on the velocity v/s altitude envelope using an LQR method. The pre-training phase guarantees that the neural network controller meets the performance specifications of the linear controllers at the design points. Online training uses a dual heuristic adaptive critic architecture that trains the two networks to meet performance specifications in the presence of nonlinearities and control failures. The control system developed is implemented for a three-degree-of-freedom longitudinal aircraft simulation. As observed from the results the adaptive control system meets performance requirements, specified in terms of the damping ratio of the phugoid and short period modes, in the presence of nonlinearities. The neural network controller also compensates for partial elevator and thrust failures. It is also observed that the neural network controller meets the performance specification for large variations in the parameters of the assumed and actual models.
Flight Dynamics
Author: Robert F. Stengel
Publisher: Princeton University Press
Published: 2022-11-01
Total Pages: 914
ISBN-13: 0691237042
DOWNLOAD EBOOKAn updated and expanded new edition of an authoritative book on flight dynamics and control system design for all types of current and future fixed-wing aircraft Since it was first published, Flight Dynamics has offered a new approach to the science and mathematics of aircraft flight, unifying principles of aeronautics with contemporary systems analysis. Now updated and expanded, this authoritative book by award-winning aeronautics engineer Robert Stengel presents traditional material in the context of modern computational tools and multivariable methods. Special attention is devoted to models and techniques for analysis, simulation, evaluation of flying qualities, and robust control system design. Using common notation and not assuming a strong background in aeronautics, Flight Dynamics will engage a wide variety of readers, including aircraft designers, flight test engineers, researchers, instructors, and students. It introduces principles, derivations, and equations of flight dynamics as well as methods of flight control design with frequent reference to MATLAB functions and examples. Topics include aerodynamics, propulsion, structures, flying qualities, flight control, and the atmospheric and gravitational environment. The second edition of Flight Dynamics features up-to-date examples; a new chapter on control law design for digital fly-by-wire systems; new material on propulsion, aerodynamics of control surfaces, and aeroelastic control; many more illustrations; and text boxes that introduce general mathematical concepts. Features a fluid, progressive presentation that aids informal and self-directed study Provides a clear, consistent notation that supports understanding, from elementary to complicated concepts Offers a comprehensive blend of aerodynamics, dynamics, and control Presents a unified introduction of control system design, from basics to complex methods Includes links to online MATLAB software written by the author that supports the material covered in the book
Improved Methods in Neural Network-based Adaptive Output Feedback Control, with Applications to Flight Control
Author: Nakwan Kim
Publisher:
Published: 2003
Total Pages:
ISBN-13:
DOWNLOAD EBOOKUtilizing the universal approximation property of neural networks, we develop several novel approaches to neural network-based adaptive output feedback control of nonlinear systems, and illustrate these approaches for several flight control applications. In particular, we address the problem of non-affine systems and eliminate the fixed point assumption present in earlier work. All of the stability proofs are carried out in a form that eliminates an algebraic loop in the neural network implementation. An approximate input/output feedback linearizing controller is augmented with a neural network using input/output sequences of the uncertain system. These approaches permit adaptation to both parametric uncertainty and unmodeled dynamics. All physical systems also have control position and rate limits, which may either deteriorate performance or cause instability for a sufficiently high control bandwidth. Here we apply a method for protecting an adaptive process from the effects of input saturation and time delays, known as "pseudo control hedging". This method was originally developed for the state feedback case, and we provide a stability analysis that extends its domain of applicability to the case of output feedback. The approach is illustrated by the design of a pitch-attitude flight control system for a linearized model of an R-50 experimental helicopter, and by the design of a pitch-rate control system for a 58-state model of a flexible aircraft consisting of rigid body dynamics coupled with actuator and flexible modes. A new approach to augmentation of an existing linear controller is introduced. It is especially useful when there is limited information concerning the plant model, and the existing controller. The approach is applied to the design of an adaptive autopilot for a guided munition. Design of a neural network adaptive control that ensures asymptotically stable tracking performance is also addressed.
Handbook of Intelligent Control
Author: David A. White
Publisher: Van Nostrand Reinhold Company
Published: 1992
Total Pages: 600
ISBN-13:
DOWNLOAD EBOOKThis handbook shows the reader how to develop neural networks and apply them to various engineering control problems. Based on a workshop on aerospace applications, this tutorial covers integration of neural networks with existing control architectures as well as new neurocontrol architectures in nonlinear control.
System Identification for Adaptive Nonlinear Flight Control Using Neural Networks
Author: Dennis J. Linse
Publisher:
Published: 1994
Total Pages: 732
ISBN-13:
DOWNLOAD EBOOKReinforcement Learning and Approximate Dynamic Programming for Feedback Control
Author: Frank L. Lewis
Publisher: John Wiley & Sons
Published: 2013-01-28
Total Pages: 498
ISBN-13: 1118453972
DOWNLOAD EBOOKReinforcement learning (RL) and adaptive dynamic programming (ADP) has been one of the most critical research fields in science and engineering for modern complex systems. This book describes the latest RL and ADP techniques for decision and control in human engineered systems, covering both single player decision and control and multi-player games. Edited by the pioneers of RL and ADP research, the book brings together ideas and methods from many fields and provides an important and timely guidance on controlling a wide variety of systems, such as robots, industrial processes, and economic decision-making.
Neural Network Control of Nonlinear Discrete-Time Systems
Author: Jagannathan Sarangapani
Publisher: CRC Press
Published: 2018-10-03
Total Pages: 624
ISBN-13: 1420015451
DOWNLOAD EBOOKIntelligent 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.
