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基于旋转矩阵的预设时间航天器编队姿态协同控制 被引量:1

Rotation matrix-based prescribed-time attitude cooperative control for spacecraft formation
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摘要 由多个航天器组成的编队系统对复杂的环境往往具有较高的适应性和容错性,能更高效率地完成单航天器难以完成的任务。因此主要针对多航天器系统的姿态协同控制问题,提出一种基于旋转矩阵的预设时间控制算法。首先,为了避免航天器姿态建模的奇异性和模糊性问题,采用旋转矩阵对航天器的姿态进行统一描述,同时结合有向的通信拓扑对航天器姿态协同控制系统进行建模。其次,为赋予系统可控的收敛速度,提出一种基于滑模的预设时间控制算法。该算法的引入使得航天器编队系统的收敛时间可以在合理的范围内任意给定。此外,为了实现系统对参数摄动和外部干扰的鲁棒性,采用神经网络和自适应算法对不确定性进行在线估计与补偿。最后,通过理论分析和数值仿真验证了所提预设时间控制算法的有效性。 The formation system with multiple spacecraft commonly has high adaptability and fault tolerance to the complex environment and is able to accomplish tasks more efficiently than a single one.Therefore,for the attitude cooperative control problem of multi-spacecraft system,a rotation matrix-based prescribed-time control algorithm is proposed in this paper.Firstly,in order to avoid the problem of singularity and fuzziness inherent in spacecraft attitude modeling,the rotation matrix is used to describe the spacecraft attitude uniformly,while the spacecraft attitude cooperative control system is modeled based on the directed communication topology.Secondly,a sliding mode-based prescribed-time control algorithm is proposed to endow the system with a controllable convergence rate.The introduction of the designed algorithm renders it possible for the formation to prescribe its settling time arbitrarily in a reasonable range.In addition,neural networks and self-adaptive algorithm are adopted to estimate and compensate for the uncertainties online to ensure the robustness against the external disturbances of parameter perturbation.Finally,the effectiveness of the proposed control scheme is verified by theoretical analysis and numerical simulations.
作者 李文静 刘成胜 蒲爱香 张文娇 LI Wenjing;LIU Chengsheng;PU Aixiang;ZHANG Wenjiao(Xi'an Aerospace Propulsion Institute,Xi'an 710100,China)
出处 《导航定位与授时》 CSCD 2023年第5期63-71,共9页 Navigation Positioning and Timing
关键词 编队 姿态协同控制 旋转矩阵 预设时间 有向通信拓扑 滑模 神经网络 自适应算法 Formation Attitude cooperative control Rotation matrix Prescribed-time Directed communication topology Sliding mode Neural networks Self-adaptive algorithm
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