Adaptive neural tracking control for near-space vehicles with stochastic disturbances

Yan, Xiaohui; Wu, Qingxian; Shao, Shuyi

doi:10.1177/1729881417703777

Cited by 7 publications

(2 citation statements)

References 63 publications

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“…In [7], the radial basis function neural network (RBFNN) was introduced to handle parameter perturbations and disturbances, while an adaptive sliding mode control law is further developed in the backstepping design to enhance the robustness of closed-loop systems. In [8], the RBFNN was also utilized to approximate the uncertainties. The disturbance was assumed to be stochastic, and an adaptive controller was then proposed under the stochastic Lyapunov stability framework.…”

Section: Introductionmentioning

confidence: 99%

Switching disturbance rejection attitude control of near space vehicles with variable structure

Gong

Wang

Dong

2019

Journal of Systems Engineering and Electronics

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A switching disturbance rejection attitude control law is proposed for a near space vehicle (NSV) with variable structure. The multiple flight modes, system uncertainties and disturbances of the NSV are taken into account based on switched nonlinear systems. Compared with traditional backstepping design methods, the proposed method utilizes the added integrals of attitude angle and angular rate tracking errors to further decrease the tracking errors. Moreover, to reduce the computation complexity, a rapid convergent differentiator is employed to obtain the derivative of the virtual control command. Finally, for disturbance rejection, based on the idea from the extended state observer (ESO), two disturbance observers are designed by using non-smooth functions to estimate the disturbances in the switched nonlinear systems. All signals of the closed-loop system are proven to be uniformly ultimately bounded under the Lyapunov function framework. Simulation results demonstrate the effectiveness of the proposed control scheme.

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Section: Introductionmentioning

confidence: 99%

Switching disturbance rejection attitude control of near space vehicles with variable structure

Gong

Wang

Dong

2019

Journal of Systems Engineering and Electronics

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“…An adaptive neural tracking controller for NSV with stochastic disturbances and unknown parametric uncertainties was designed in Yan et al. 13 In Yang and Chen, 14 an adaptive attitude control strategy with prescribed performance was applied to NSV with system modeling errors, external unknown disturbances, and control input nonlinearities, and the boundedness and transient performance of the tracking error have been guaranteed. In a word, many attitude controllers have been designed to ensure the tracking error converge to a small range and obtain satisfactory control performance.…”

Section: Introductionmentioning

confidence: 99%

Neural network based optimal adaptive attitude control of near-space vehicle with system uncertainties and disturbances

Xia

2018

Proceedings of the Institution of Mechanical Engineers, Part G:

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In this paper, a neural network based optimal adaptive attitude control scheme is derived for the near-space vehicle with uncertainties and external time-varying disturbances. Firstly, radial basis function neural network (RBFNN) approximation method and nonlinear disturbance observer (NDO) are used to tackle the system uncertainties and external disturbances, respectively. Subsequently, a feedforward control input under backstepping control frame with RBFNN and NDO is designed to transform the optimal tracking control problem into an optimal stabilization problem. Then, a single online approximation based adaptive method is used to learn the Hamilton–Jacobi–Bellman equation to obtain the corresponding optimal controller. As a result, the compound controller consists of feedforward control input and optimal controller which can ensure that the near-space vehicle attitude angles are able to track reference signals in an optimal way. Lyapunov stability analysis method is used to show that all the closed-loop system signals are uniformly ultimately bounded. Finally, simulation results show the effectiveness of the proposed optimal attitude control scheme.

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Robust adaptive dynamic surface control for hypersonic vehicles

Gao

Gutiérrez

et al. 2018

Nonlinear Dyn

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Adaptive neural tracking control for near-space vehicles with stochastic disturbances

Cited by 7 publications

References 63 publications

Switching disturbance rejection attitude control of near space vehicles with variable structure

Switching disturbance rejection attitude control of near space vehicles with variable structure

Neural network based optimal adaptive attitude control of near-space vehicle with system uncertainties and disturbances

Robust adaptive dynamic surface control for hypersonic vehicles

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