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In this paper, a unified neural control scheme is presented for the output-constrained trajectory tracking of space manipulator under unknown parameters and external perturbations. By utilizing the backstepping control technique as the main framework, the proposed controller is developed with the help of neural network (NN) and tan-type barrier Lyapunov function (BLF). The NN is introduced to identify the unknown part in the dynamic model of the space manipulator. Benefiting from the neural identification, the proposed controller is model-free and insensitive to external perturbations. Moreover, the BLF is adopted to guarantee the position tracking errors never exceed the predefined output constraints. Different from log-type BLF, the tan-type BLF is employed for the control design, which makes the proposed controller universal for the cases with and without considering the output constraints. The semiglobal uniform ultimate boundedness of the resulting closed-loop system is strictly obtained through stability argument. All error variables in the closed-loop system can eventually stabilize to the small residual sets about zero under the proposed controller. Lastly, simulations and comparisons are given to demonstrate the effectiveness and excellent tracking performance of the proposed control scheme. [ABSTRACT FROM AUTHOR]

This article addresses the challenging problem of fixed-time output-constrained synchronization for master–slave chaotic financial systems with unknown parameters and perturbations. A fixed-time neural adaptive control approach is originally proposed with the aid of the barrier Lyapunov function (BLF) and neural network (NN) identification. The BLF is introduced to preserve the synchronization errors always within the predefined output constraints. The NN is adopted to identify the compound unknown item in the synchronization error system. Unlike the conventional NN identification, the concept of indirect NN identification is employed, and only a single adaptive learning parameter is required to be adjusted online. According to the stability argument, the proposed controller can ensure that all error variables in the closed-loop system regulate to the minor residual sets around zero in fixed time. Finally, simulations and comparisons are conducted to verify the efficiency and benefits of the proposed control strategy. It can be concluded from the simulation results that the proposed fixed-time neural adaptive controller is capable of achieving better synchronization performance than the compared linear feedback controller. [ABSTRACT FROM AUTHOR]

In this paper, we present an adaptive control strategy for the output-constrained attitude tracking of rigid spacecraft under unknown inertia and disturbances. The designed controller is recursively constructed under the framework of the backstepping method, which involves an auxiliary stabilizing law and a practical control law. In the auxiliary stabilizing law design, the log-type barrier Lyapunov function (BLF) is embedded to tackle the time-varying output constraints. In addition, in the practical control law design, the element-wise parametric adaptive laws are adopted to identify the unknown inertia and disturbances by introducing a linear mapping operator. The stability evaluation indicates that the overall closed-loop system is semi-globally uniformly ultimately bounded (SGUUB) and all error variables can eventually regulate to the minor fields about zero. Meanwhile, the spacecraft attitude tracking errors can be preserved in the preassigned output constraints. Lastly, the efficiency and strong robustness of the presented control strategy are demonstrated through competitive simulations. [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]