Neural Adaptive Fault-Tolerant Control for Attitude Tracking of Spacecraft

This paper investigates the problem of attitude tracking control with prescribed tracking performance for a spacecraft exposed to unknown inertial matrix and actuator failure. Based on the prescribed tracking performance technique, an adaptive fault-tolerant controller is adopted by taking backstepping control and neural-network into consideration. By estimating the bounds of time-varying parameters induced by fault uncertainties, the influence of actuator faults is successfully compensated for. By estimating the norm of the optimal weight vector of neural networks, there is only one parameter needs to be estimated, which significantly reduces the computational burden. It is proved that the proposed controller can guarantee that all closed-loop signals are bounded and the tracking errors are converged into an arbitrarily small residual set with prescribed performance. Simulation results are presented to demonstrate the effectiveness of the proposed control scheme.

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