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This brief presents a finite‐time output feedback control scheme for the velocity‐free attitude stabilization of rigid spacecraft under inertia uncertainties and disturbances. Note that either the finite‐time velocity observer or the finite‐time extended state observer contains the control torques, which makes the transient performance of the observer‐based output feedback control difficult to be regulated. Alternatively, the filter‐based output feedback control can overcome such problem naturally since the finite‐time velocity filter does not involve the control torques in its structure. Different from the existing finite‐time velocity filters, a finite‐time extended state filter is introduced in this work to estimate the pseudo angular velocity and total uncertain item simultaneously. Then, the developed controller is synthesized based on the recovered information, which does not require the angular velocity for feedback and has the excellent uncertainty and disturbance compensation capability. The global finite‐time stability of the resultant closed‐loop system is evaluated through the Lyapunov direct methodology and homogeneous system theory. Lastly, comparative simulations are carried out to examine the efficiency and superiority of the presented control scheme. [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]

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]