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Book Direct Adaptive Aircraft Control Using Dynamic Cell Structure Neural Networks

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...

Book Direct Adaptive Aircraft Control Using Dynamic Cell Structure Neural Networks

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:

Book Fully Tuned Radial Basis Function Neural Networks for Flight Control

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.

Book Reducing Wind Tunnel Data Requirements Using Neural Networks

Download or read book Reducing Wind Tunnel Data Requirements Using Neural Networks written by James Carl Ross and published by . This book was released on 1997 with total page 20 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Book A Generic Guidance and Control Structure for Six Degree Of Freedom Conceptual Aircraft Design

Download or read book A Generic Guidance and Control Structure for Six Degree Of Freedom Conceptual Aircraft Design written by National Aeronautics and Space Administration (NASA) and published by Createspace Independent Publishing Platform. This book was released on 2018-06-24 with total page 26 pages. Available in PDF, EPUB and Kindle. Book excerpt: A control system framework is presented for both real-time and batch six-degree-of-freedom simulation. This framework allows stabilization and control with multiple command options, from body rate control to waypoint guidance. Also, pilot commands can be used to operate the simulation in a pilot-in-the-loop environment. This control system framework is created by using direct vehicle state feedback with nonlinear dynamic inversion. A direct control allocation scheme is used to command aircraft effectors. Online B-matrix estimation is used in the control allocation algorithm for maximum algorithm flexibility. Primary uses for this framework include conceptual design and early preliminary design of aircraft, where vehicle models change rapidly and a knowledge of vehicle six-degree-of-freedom performance is required. A simulated airbreathing hypersonic vehicle and a simulated high performance fighter are controlled to demonstrate the flexibility and utility of the control system. Cotting, M. Christopher and Cox, Timothy H. Armstrong Flight Research Center NASA/TM-2005-212866, H-2596, AIAA Paper 2005-0032

Book Neural Network Based Adaptive Control for Nonlinear Dynamic Regimes

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.

Book Reconfigurable Control with Neural Network Augmentation for a Modified F 15 Aircraft

Download or read book Reconfigurable Control with Neural Network Augmentation for a Modified F 15 Aircraft written by National Aeronautics and Space Adm Nasa and published by Independently Published. This book was released on 2018-09-16 with total page 30 pages. Available in PDF, EPUB and Kindle. Book excerpt: Description of the performance of a simplified dynamic inversion controller with neural network augmentation follows. Simulation studies focus on the results with and without neural network adaptation through the use of an F-15 aircraft simulator that has been modified to include canards. Simulated control law performance with a surface failure, in addition to an aerodynamic failure, is presented. The aircraft, with adaptation, attempts to minimize the inertial cross-coupling effect of the failure (a control derivative anomaly associated with a jammed control surface). The dynamic inversion controller calculates necessary surface commands to achieve desired rates. The dynamic inversion controller uses approximate short period and roll axis dynamics. The yaw axis controller is a sideslip rate command system. Methods are described to reduce the cross-coupling effect and maintain adequate tracking errors for control surface failures. The aerodynamic failure destabilizes the pitching moment due to angle of attack. The results show that control of the aircraft with the neural networks is easier (more damped) than without the neural networks. Simulation results show neural network augmentation of the controller improves performance with aerodynamic and control surface failures in terms of tracking error and cross-coupling reduction.Burken, John J. and Williams-Hayes, Peggy and Kaneshige, John T. and Stachowiak, Susan J.Ames Research Center; Armstrong Flight Research Center; Johnson Space CenterAUGMENTATION; CONTROLLERS; F-15 AIRCRAFT; NEURAL NETS; ANGLE OF ATTACK; PITCHING MOMENTS; ROLL; SIDESLIP; SIMULATION; CONTROL SURFACES

Book Neural Network Based Adaptive Control of Uncertain and Unknown Nonlinear 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 2001 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Our main accomplishment this past year has been to finalize and apply two approaches to output feedback adaptive control. The first is a direct adaptive approach, while the second uses a new error state observe. Both approaches overcome the limitation of earlier adaptive state observer based methods, which require that the order of the plant be known, and impose severe restrictions on the relative degree of regulated output variables. Within this context, we also have continued to exploit our approach for adaptive hedging' of actuator limits, which was the highlight of last year's report. We have also made some progress in the area of decentralized adaptive control. Our most significant interactions have been with NASA Marshall, NASA Ames, Wright Patterson AFB, Eglin AFB, Boeing and Lockheed.

Book Research in Neural Network Based Adaptive Control

Download or read book Research in Neural Network Based Adaptive Control written by and published by . This book was released on 2000 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The most significant theoretical accomplishment has been the development of a new approach for dealing with control limits and nonlinearities in adaptive systems. This approach both prevents the Maptire system from doing harm to an otherwise stable system, and also allows adaptation to continue while the control is saturated. We regard this as a major step towards flight certification of adaptive controllers. The approach is more general in that it permits a broad class of input nonlinearities, including such effects as discrete and bang/bang control. In the area of output feedback, we continue to refine our curlier work, and have begun to take steps in the direction of decentralized adaptive systems in a state feedback setting. Our most significant interactions have been with NASA Marshall and NASA Ames. In particular, we arc fully exploiting our research in limited authority adaptive control in the areas of autopilot design for launch vehicles, and propulsion control for commercial aircraft subject to partial or total loss of conventional flight control.

Book Adaptive Neural Network Control of Aircraft

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:

Book Event sampled Direct Adaptive Neural Network Control of Uncertain Strict feedback System with Application to Quadrotor Unmanned Aerial Vehicle

Download or read book Event sampled Direct Adaptive Neural Network Control of Uncertain Strict feedback System with Application to Quadrotor Unmanned Aerial Vehicle written by Nathan Szanto and published by . This book was released on 2016 with total page 114 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Neural networks (NNs) are utilized in the backstepping approach to design a control input by approximating unknown dynamics of the strict-feedback nonlinear system with event-sampled inputs. The system state vector is assumed to be unknown and an observer is used to estimate the state vector. By using the estimated state vector and backstepping design approach, an event-sampled controller is introduced. As part of the controller design, first, input-to-state-like stability (ISS) for a continuously sampled controller that has been injected with bounded measurement errors is demonstrated and, subsequently, an event-execution control law is derived such that the measurement errors are guaranteed to remain bounded. Lyapunov theory is used to demonstrate that the tracking errors, the observer estimation errors, and the NN weight estimation errors for each NN are locally uniformly ultimately bounded (UUB) in the presence bounded disturbances, NN reconstruction errors, as well as errors introduced by event-sampling. Simulation results are provided to illustrate the effectiveness of the proposed controllers. Subsequently, the output-feedback neural network (NN) controller that was presented above is considered for an underactuated quadrotor UAV application. The flexibility for the control of a quadrotor UAV is extended by incorporating notions of event-sampling and by designing an appropriate event-execution law. First, the continuously sampled controller is considered in the presence of bounded measurement errors and it is shown that the system generates a local ISS-like Lyapunov function. Next, by designing an appropriate event-execution law, the measurement errors that result from event-sampling are shown to be bounded for all time. Finally, the effectiveness of the proposed event-sampled controller is demonstrated with simulation results"--Abstract, page iv.