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- W4313563048 abstract "Modern high-performance fighter aircraft are required to be designed to cater for extreme non-linearities based deterioration which may arise out of various failures or damage scenarios during flight. While it is known that conventional gain scheduling-based flight control design does not cater for such failure scenarios effectively therefore, a combination of dynamic inversion and neural network-based approach was found to be attractive, however till date only offline training-based methods were demonstrated. This paper demonstrates design of an online adaptive neural networks based intelligent reconfigurable non-linear control law to prevent loss of control of a fighter aircraft which may have undergone certain faults and failures inflight. The aim is to provide sufficient stability to the aircraft so that it can continue to fly safely and land at the nearest available runway. Design is carried out using a Non-linear Dynamic Inversion (NDI) which performs plant inversion to arrive at the virtual control commands for the desired longitudinal and lateral directional dynamics as generated by explicit model following controller. The intelligence part is obtained from design of single hidden layer neural network with online weight adaption to minimize the error between actual and desired dynamics. This adaptive neural network together with NDI and CA provides stability to aircraft in the event of minor faults/damages." @default.
- W4313563048 created "2023-01-06" @default.
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- W4313563048 date "2022-11-26" @default.
- W4313563048 modified "2023-10-14" @default.
- W4313563048 title "Design of Intelligent Reconfigurable Control Laws towards Loss of Control Prevention of an Impaired High Performance Fighter Aircraft" @default.
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- W4313563048 doi "https://doi.org/10.1109/icecie55199.2022.10000337" @default.
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