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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]

Unmanned aerial vehicles are subject to complex aerodynamic or hydrodynamic forces and various uncertainties, making their dynamic modeling difficult. Hence, in these systems, it is preferred to apply control techniques whose closed-loop systems can predict the system's uncertain dynamics and provide acceptable performance. To this end, this paper exploits a new neural network-based optimal mixed H 2 / H ∞ control for a modified unmanned aerial vehicle to accomplish trajectory tracking missions. Firstly, the dynamic model of the modified unmanned aerial vehicles is presented. Then, the design procedure of the new controller is delineated. In this approach, H ∞ attenuates the effect of uncertainties and through H 2 the consumed control energy is minimized. Similar to real-world applications, it is supposed that there are control input constraints and external disturbances. The neural networks are also employed to estimate all uncertainties. The numerical simulations for two different desired paths are demonstrated. Simulation results illustrate the efficient performance of the proposed control technique in the presence of external disturbances, dynamic uncertainties, and control input constraints. [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]