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- W2328290411 abstract "Launch vehicle attitude control is a complex nonlinear problem that is compounded by several internal and external factors such as modeling uncertainties, elastic dynamics, fuel sloshing and unstable aerodynamics. Adaptive control strategies that utilize a neural network augmented to a linear controller have the capability to minimize the effect of these uncertainties and disturbances. In this paper, the architecture and learning mechanism of a new type of recurrent neural network known as the Variable Memory Recurrent Neural Network (VMRNN) is presented. This network, when used in conjunction with a proportional integral derivative (PID) controller for the attitude control of a Nanosat Launch Vehicle, offers improved transient response characteristics when encountering uncertainty in the dynamic model and external disturbances. Further, a comparison of the VMRNN augmented PID controller with a traditional Feedforward Neural Network (FFNN) and Recurrent Neural Network (RNN) augmented PID controller demonstrates its improved learning capability." @default.
- W2328290411 created "2016-06-24" @default.
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- W2328290411 date "2015-01-02" @default.
- W2328290411 modified "2023-09-27" @default.
- W2328290411 title "Variable Memory Recurrent Neural Networks For Launch Vehicle Attitude Control" @default.
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- W2328290411 doi "https://doi.org/10.2514/6.2015-1774" @default.
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