Abstract
This paper presents an adaptive finite time tracking control design for a spacecraft with uncertainties and input saturation. Radial basis function neural networks (RBFNNs) are used to approximate the unknown uncertainties and external disturbances. The input saturation effect is compensated by adding an auxiliary signal. State feedback finite time tracking controller is then proposed based on the finite time control principle and backstepping technique. Consequently, attitude tracking of an uncertain spacecraft with the finite time convergent property is achieved even in the presence of input saturation. Stability of the closed-loop system is analyzed via Lyapunov direct method. Simulation studies are conducted to examine the effectiveness of the proposed control.
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