Control of Reusable Launch Vehicle Using Neuro-adaptive Approach
This work explores neural networks (NN) based control approach to nonlinear flight systems in the presence of disturbances and uncertainties. Neuro-adaptive control incorporating with two neural network (NN) units is proposed to cope with NN reconstruction error and other lumped system uncertainties. It is shown that with the proposed strategy, the angles of attack, sideslip and body-axis roll of the vehicle are effectively controlled. The method does not involve analytical estimation of the upper bounds on ideal weights, reconstruction error, or nonlinear functions. Stable on-line weight-tuning algorithms are derived based on Lyapunov’s stability theory. Analyses and simulations confirm the effectiveness of the proposed control scheme.
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