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In this paper, a neural adaptive fault-tolerant control scheme is proposed for the integrated attitude and position control of spacecraft proximity operations in the presence of unknown parameters, disturbances, and actuator faults. The proposed controller is made up of a relative attitude control law and a relative position control law. Both the relative attitude control law and relative position control law are designed by adopting the neural networks (NNs) to approximate the upper bound of the lumped unknowns. Benefiting from the indirect neural approximation, the proposed controller does not need any model information for feedback. In addition, only two adaptive parameters are required for the indirect neural approximation, and the online calculation burden of the proposed controller is therefore significantly reduced. Lyapunov analysis shows that the overall closed-loop system is ultimately uniformly bounded. The proposed controller can ensure the relative attitude, angular velocity, position, and velocity stabilize into the small neighborhoods around the origin. Lastly, the effectiveness and superior performance of the proposed control scheme are confirmed by a simulated example. [ABSTRACT FROM AUTHOR]

This article aims to propose a novel neural adaptive control approach for the fixed-time attitude tracking of space vehicle subject to constrained outputs. The proposed controller is recursively synthesized by integrating with the barrier Lyapunov function (BLF) and neural approximation under the fixed-time backstepping control framework. The whole control design procedure involves two steps. In the first step, the virtual control signal is designed by adopting the BLF to guarantee the attitude tracking errors always within the predefined output constraints. In the second step, the actual control signal is designed by augmenting the neural network (NN) to approximate the uncertain term caused by unknown dynamics and disturbances. Theoretical analysis shows that the proposed controller can ensure all error signals in the closed-loop system stabilize to the small regions about the origin in fixed time even under unknown dynamics and disturbances. The effectiveness and excellent performance of the proposed control approach are validated by simulations and comparisons. [ABSTRACT FROM AUTHOR]