Department of Computer Science, College of Computer Sciences and Information Technology, King Faisal University, Al-Ahsa 31982, Saudi Arabia.
School of Aerospace Engineering, Beijing Institute of Technology, Beijing 100081, China.
Sensors (Basel). 2022 Feb 23;22(5):1726. doi: 10.3390/s22051726.
In this paper, a neural adaptive fault-tolerant control scheme is proposed for the integrated attitude and position control of spacecraft proximity operations in the presence of unknown parameters, disturbances, and actuator faults. The proposed controller is made up of a relative attitude control law and a relative position control law. Both the relative attitude control law and relative position control law are designed by adopting the neural networks (NNs) to approximate the upper bound of the lumped unknowns. Benefiting from the indirect neural approximation, the proposed controller does not need any model information for feedback. In addition, only two adaptive parameters are required for the indirect neural approximation, and the online calculation burden of the proposed controller is therefore significantly reduced. Lyapunov analysis shows that the overall closed-loop system is ultimately uniformly bounded. The proposed controller can ensure the relative attitude, angular velocity, position, and velocity stabilize into the small neighborhoods around the origin. Lastly, the effectiveness and superior performance of the proposed control scheme are confirmed by a simulated example.
本文提出了一种针对航天器近距离操作中存在未知参数、干扰和执行器故障的综合姿态和位置控制的神经自适应容错控制方案。所提出的控制器由相对姿态控制律和相对位置控制律组成。相对姿态控制律和相对位置控制律均采用神经网络（NN）来逼近集中未知项的上界。受益于间接神经逼近，所提出的控制器不需要任何反馈模型信息。此外，间接神经逼近仅需要两个自适应参数，因此大大降低了所提出控制器的在线计算负担。Lyapunov 分析表明，整个闭环系统最终是一致有界的。所提出的控制器可以确保相对姿态、角速度、位置和速度稳定在原点附近的小邻域内。最后，通过仿真示例验证了所提出的控制方案的有效性和优越性能。
