Selected Flight Test Results for Online Learning Neural Network-Based Flight Control System

Williams-Hayes, Peggy S.

doi:10.2514/6.2004-6283

Cited by 16 publications

(12 citation statements)

References 6 publications

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“…The principal objective of SDC was to minimize the impact of failures or damage on the pilot's ability to control the craft and the project was successful in this regard. Direct methods such as [3,4] have been successfully flight tested on a highly modified F-15 known as NASA 837 [5,6], the X-36 [7], and JDAM guided munitions [8,9]. Among the benefits demonstrated have been maintenance of flying qualities in the presence of control effector failures, the ability to compensate for large modeling errors and reduced control design time.…”

Section: Introductionmentioning

confidence: 97%

Progress in Guidance and Control Research for Space Access and Hypersonic Vehicles

Doman

Oppenheimer

Bolender

2006

Proceedings of the 45th IEEE Conference on Decision and Control

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Over the past decade, both NASA and the U.S. Air Force have directed significant efforts aimed at reducing the cost of access to space as well as improving the reliability and safety of launch vehicles. From a guidance and control perspective, efforts have focused on the development of fault tolerant autonomous systems that can recover vehicles from failures or damage when physically possible. The state of the art is summarized in this manuscript and some of the challenges preventing widespread use of some of the techniques are discussed. The integration of vehicle health management systems with adaptive guidance and control is also discussed.

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Section: Introductionmentioning

confidence: 97%

Progress in Guidance and Control Research for Space Access and Hypersonic Vehicles

Doman

Oppenheimer

Bolender

2006

Proceedings of the 45th IEEE Conference on Decision and Control

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“…Moreover, intelligent flight control system (IFCS) program [11][12][13][14][15][16] was performed from 2002 to 2006 and integrated adaptive guidance and control (IAG&C) program was held during the early 2000s [17][18][19]. Many reconfigurable control approaches and technologies have been demonstrated in these programs, such as NDI, backstepping, MPC, parameter identification (PID), and NN.…”

Section: Introductionmentioning

confidence: 99%

Survey on nonlinear reconfigurable flight control

Jiang

et al. 2013

J. of Syst. Eng. Electron.

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An overview on nonlinear reconfigurable flight control approaches that have been demonstrated in flight-test or highfidelity simulation is presented. Various approaches for reconfigurable flight control systems are considered, including nonlinear dynamic inversion, parameter identification and neural network technologies, backstepping and model predictive control approaches. The recent research work, flight tests, and potential strength and weakness of each approach are discussed objectively in order to give readers and researchers some reference. Finally, possible future directions and open problems in this area are addressed.

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“…The first flight phase, known as generation one (Gen 1), required aerodynamic parameter identification and is not discussed in this report (please see [1] for a discussion on the Gen 1 flight phase). The second flight phase, known as generation two (Gen 2), evaluates a neural flight control system that can provide adaptive control without explicit parameter identification.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Control Using Neural Network Augmentation for a Modified F-15 Aircraft

Burken

Williams-Hayes

Kaneshige

et al. 2006

2006 14th Mediterranean Conference on Control and Automation

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Abstract-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.

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Selected Flight Test Results for Online Learning Neural Network-Based Flight Control System

Cited by 16 publications

References 6 publications

Progress in Guidance and Control Research for Space Access and Hypersonic Vehicles

Progress in Guidance and Control Research for Space Access and Hypersonic Vehicles

Survey on nonlinear reconfigurable flight control

Adaptive Control Using Neural Network Augmentation for a Modified F-15 Aircraft

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