Reinforcement Learning Based Dynamic Inverse Attitude Control of Near-space Vehicle

2020 ◽  
Author(s):  
Yaohua Shen ◽  
Mou Chen
Keyword(s):  
Reinforcement Learning ◽  
Attitude Control ◽  
Space Vehicle ◽  
Near Space ◽  
Dynamic Inverse

PI Predictive Functional Attitude Control of Near Space Vehicle

2015 ◽  
Vol 8 (8) ◽  
pp. 409-424 ◽  
Author(s):  
Zhenyu Lu ◽  
Kai Li ◽  
Tingya Yang ◽  
Wei Guo ◽  
Jin Wang
Keyword(s):  
Attitude Control ◽  
Space Vehicle ◽  
Near Space

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

2018 ◽  
Vol 233 (2) ◽  
pp. 641-656 ◽  
Author(s):  
Rongsheng Xia ◽  
Mou Chen ◽  
Qiangxian Wu
Keyword(s):  
Neural Network ◽  
Attitude Control ◽  
Feedforward Control ◽  
Space Vehicle ◽  
Adaptive Method ◽  
Optimal Controller ◽  
Control Input ◽  
Near Space ◽  
Control Scheme ◽  
System Uncertainties

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.

scholarly journals Deformation Reconstruction and High-Precision Attitude Control of a Launch Vehicle Based on Strain Measurements

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Liang Zhuang ◽  
Zhang Yulin
Keyword(s):  
High Precision ◽  
Attitude Control ◽  
Control Method ◽  
Launch Vehicles ◽  
Bending Vibration ◽  
Inverse Control ◽  
Deformation Parameters ◽  
Control Framework ◽  
Fbg Sensors ◽  
Dynamic Inverse

The development of launch vehicles has led to higher slenderness ratios and higher structural efficiencies, and the traditional control methods have difficulty in meeting high-quality control requirements. In this paper, an incremental dynamic inversion control method based on deformation reconstruction is proposed to achieve high-precision attitude control of slender launch vehicles. First, the deformation parameters of a flexible rocket are obtained via fiber Bragg grating (FBG) sensors. The deformation and attitude information is introduced into the incremental dynamic inverse control loop, and an attitude control framework that can alleviate bending vibration and deformation is established. The simulation results showed that the proposed method could accurately reconstruct the shapes of flexible launch vehicles with severe vibration and deformation, which could improve the accuracy and stability of attitude control.

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