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This article presents an indirect neural-based finite-time integral sliding mode control algorithm for the reference trajectory tracking guidance of Mars entry vehicle under uncertainties. The proposed controller is developed as a combination of finite-time integral sliding mode controller and indirect neural identification. The finite-time integral sliding mode controller is designed by constructing a new type of finite-time integral sliding mode surface to prevent the singularity problem. Moreover, the neural network (NN) is combined with the finite-time integral sliding mode controller to identify the lumped uncertainty and attenuate the chattering phenomenon. Particularly, the concept of indirect neural identification is adopted and only a single adaptive parameter is required to be learned online. In this way, the proposed controller is not only strongly robust against aerodynamic and density uncertainties, but also computationally simple for onboard implementations. Stability argument indicates that the proposed controller can ensure the radial distance tracking error and its time differentiation regulate to the small residual sets around zero in finite time. Lastly, the effectiveness and excellent guidance performance of the proposed control algorithm are demonstrated through simulation studies on a Mars Science Laboratory-type (MSL-type) entry vehicle. [ABSTRACT FROM AUTHOR]

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]

In this paper, the convergence guaranteed attitude stabilization of underactuated spacecraft using two control torques is investigated. By introducing the (w , z) parameters to describe the attitude motion of the underactuated spacecraft, a unified integrated attitude kinematic and dynamic model is originally derived, based on which the controllers can be directly designed for the two actuated axes. Then, three types of controllers are designed to stabilize the attitude and angular velocity along the two actuated axes to zero in fixed time. After that, it is strictly evaluated that the attitude and angular velocity along the underactuated axis can eventually stabilize to the constant values. In this way, the convergence property of the designed controllers can be guaranteed. The utilized controllers are the fixed-time proportional-differential (PD)-like controller, the fixed-time nonsingular terminal sliding mode (NTSM) controller, and the fixed-time integral sliding mode (ISM) controller, respectively. Among them, the fixed-time ISM controller is best since it is strongly robust against disturbances and has no singularity problem naturally, which overcomes the drawbacks of the fixed-time PD-like controller and the fixed-time NTSM controller simultaneously. Lastly, simulated studies are executed to examine the derived results. [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]