Neural-Network-Based Adaptive Singularity-Free Fixed-Time Attitude Tracking Control for Spacecrafts
Neural-Network-Based Adaptive Singularity-Free Fixed-Time Attitude Tracking Control for Spacecrafts
复制标题
基于神经网络的航天器自适应无奇点固定时间姿态跟踪控制
DOI：
10.1109/tcyb.2020.3024672
复制
发表时间：
2021-10-01
影响因子：
11.8
通讯作者：
He, Xiongxiong
中科院分区：
文献类型：
--
作者：
Chen, Qiang;Xie, Shuzong;He, Xiongxiong
针对不确定刚体航天器的姿态跟踪问题，提出了一种基于神经网络的自适应定时控制方案。提出了一种具有直接非奇异性质的无奇异定时切换函数，并在控制器设计过程中引入辅助函数来完成切换函数，避免了误差相关矩阵逆引起的潜在奇异问题。然后，设计了一种自适应神经控制器，保证姿态跟踪误差和角速度误差在固定时间内收敛到平衡点的邻域；采用该控制方案，在控制器设计中不再需要分段连续函数来避免奇异性，并对整个闭环系统在到达相位和滑动相位的定时稳定性进行了严密的理论证明。仿真结果表明了该方案的有效性和优越性。
In this article, a neural-network-based adaptive fixed-time control scheme is proposed for the attitude tracking of uncertain rigid spacecrafts. A novel singularity-free fixed-time switching function is presented with the directly nonsingular property, and by introducing an auxiliary function to complete the switching function in the controller design process, the potential singularity problem caused by the inverse of the error-related matrix could be avoided. Then, an adaptive neural controller is developed to guarantee that the attitude tracking error and angular velocity error can both converge into the neighborhood of the equilibrium within a fixed time. With the proposed control scheme, no piecewise continuous functions are required any more in the controller design to avoid the singularity, and the fixed-time stability of the entire closed-loop system in the reaching phase and sliding phase is analyzed with a rigorous theoretical proof. Comparative simulations are given to show the effectiveness and superiority of the proposed scheme.