Helicopter adaptive flight control using neural networks

Calise, Anthony J.; Kim, B.S.; Leitner, Jesse; Prasad, J. V. R.

doi:10.1109/cdc.1994.411659

Cited by 31 publications

(37 citation statements)

References 13 publications

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“…Подібний підхід розглядається і в розробці NASA, яка функціонує на базі роботи [28]. Інтелектуальна си-стема управління польотом використовує наступні під-ходи: модель-посилання, пряму адаптацію та динамічну інверсію.…”

Section: робастне та адаптивне керування з використанням інтелектуальunclassified

Review of existing control systems that are used on unmanned aerial vehicles

Міщук¹

2014

EEJET

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Section: робастне та адаптивне керування з використанням інтелектуальunclassified

Review of existing control systems that are used on unmanned aerial vehicles

Міщук¹

2014

EEJET

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“…As a prototype for this methodology we are using one of the implementations of the IFCS direct adaptive flight control system [26][27][28] . The control system we are using was implemented as a Mathworks Simulink model and was used as a research-level tool to understand neural networks in 2001.…”

Section: Methodsmentioning

confidence: 99%

Hybrid Decompositional Verification for Discovering Failures in Adaptive Flight Control Systems

Thompson

Davies

Gundy-Burlet

2010

AIAA Infotech@Aerospace 2010

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Adaptive flight control systems hold tremendous promise for maintaining the safety of a damaged aircraft and its passengers. However, most currently proposed adaptive control methodologies rely on online learning neural networks (OLNNs), which necessarily have the property that the controller is changing during the flight. These changes tend to be highly nonlinear, and difficult or impossible to analyze using standard techniques. In this paper, we approach the problem with a variant of compositional verification. The overall system is broken into components. Undesirable behavior is fed backwards through the system. Components which can be solved using formal methods techniques explicitly for the ranges of safe and unsafe input bounds are treated as white box components. The remaining black box components are analyzed with heuristic techniques that try to predict a range of component inputs that may lead to unsafe behavior. The composition of these component inputs throughout the system leads to overall system test vectors that may elucidate the undesirable behavior.

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“…Non-linear dynamic inversion method based on neural network is introduced to design the large envelope flight control and get good effect. But the robustness and the transient characteristic performance of this control system is not strong to dynamic characteristic and external disturbance of non-model system [1][2][3][4] . A neural network-sliding model flight controller is designed to overcome the shortcoming above and increase the control precision in each area respectively, and continuous switch controllers by different flight areas.…”

Section: Introductionmentioning

confidence: 99%

Model of neutral network sliding design by large envelope flight control law

Chen¹,

Xin²

2017

Proceedings of the 2nd Annual International Conference on Electronics, Electrical Engineering and Information Science (EEEIS 20

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In this paper, the neural network sliding mode control design method is studied for the large enveloping flight control law of the model with very different flight parameters. The neural network theory is used to approximate the nonlinear system and eliminate the errors brought by the approximate inversion, and the residual error is solved by sliding model control. Therefore, the nonlinear model can be approximated accurately and the robustness and anti-jamming ability of the flight control system can be improved. The simulation results show that the design of the neural network -sliding model large envelope flight controller has excellent control performance.

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Helicopter adaptive flight control using neural networks

Cited by 31 publications

References 13 publications

Review of existing control systems that are used on unmanned aerial vehicles

Review of existing control systems that are used on unmanned aerial vehicles

Hybrid Decompositional Verification for Discovering Failures in Adaptive Flight Control Systems

Model of neutral network sliding design by large envelope flight control law

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