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This article proposes a neural-network-based adaptive finite-time output constraint control scheme for attitude stabilization of rigid spacecrafts. First, a novel singularity-free terminal sliding mode variable is constructed and an auxiliary function is developed in the controller design to avoid the singularity problem.Then, an adaptive neural control law is designed to approximate the lumped uncertainty of spacecraft system including inertia uncertainties and external disturbances. Furthermore, a novel finite-time prescribed performance function is constructed for characterizing the convergence rate and steady state of the spacecraft attitude, such that the attitude can be maintained within a prescribed small region in finite time. Finally, the finite-time stability of the whole closed-loop system is analyzed by rigorous theoretical proofs, and comparative simulations are given to show the effectiveness and superiority of the proposed scheme.

This paper presents a finite-time commandfiltered approximation-free attitude tracking control for rigid spacecraft. A novel finite-time prescribed performance function (FTPPF) is first constructed to ensure that the attitude tracking errors converge to the predefined region in finite time. Then, a finite-time error compensation mechanism is constructed and incorporated into the backstepping control design, such that the differentiation of virtual control signals in recursive steps can be avoided to overcome the singularity issue. Compared with most of approximation-based attitude control methods, less computational burden and lower complexity are guaranteed by the proposed approximation-free control scheme due to the avoidance of using any function approximations. Simulation-

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