中国光学（中英文）2025，Vol.18Issue(3)：631-640,10.DOI:10.37188/CO.2024-0156
基于双敏感轴分解的检验质量刚度辨识
Identification of test mass stiffness based on dual sensitive axis decomposition
摘要
Abstract
The coupling noise between test mass stiffness and displacement,as a significant component of the residual acceleration noise,critically impacts the performance of space gravitational wave detection,making stiffness identification essential for validating and optimizing control strategies and meeting the noise sup-pression requirements.For non-coaxial test mass configurations,this paper proposes a novel identification method based on dual sensitive axis decomposition.First,a relative dynamic model between the test mass and the spacecraft is constructed,and the model parameters are decomposed along the dual sensitive axis to isolate the influence of spacecraft acceleration disturbances and predominant angular acceleration disturb-ances on the on-orbit identification.Second,utilizing on-board laser interferometers,inertial sensors,and as-sociated control loops,an on-orbit identification scheme is designed and a stiffness identification method us-ing recursive least squares is proposed.Finally,numerical simulations are performed to verify the perform-ance of the method.The experimental results demonstrate that the proposed stiffness identification method can effectively identify the stiffness of the test mass on the sensitive axis.Under the given simulation condi-tions,the mean absolute error is less than 5× 10-9 s-2,the root mean square error is less than 1.5×10-8 s-2,and the maximum steady-state error is less than 2×10-9 s-2.These findings suggest that the method can be applied to future gravitational wave science missions.关键词
空间引力波探测/检验质量/刚度辨识/双敏感轴分解/递归最小二乘Key words
space gravitational wave detection/test mass/stiffness identification/dual sensitive axis decom-position/recursive least squares分类
航空航天引用本文复制引用
汤宁标,杨中光,蔡志鸣,方子若,刘野,胡海鹰,李华旺..基于双敏感轴分解的检验质量刚度辨识[J].中国光学（中英文）,2025,18(3):631-640,10.基金项目
国家重点研发计划(No.2020YFC2200901)Supported by the National Key Research and Development Program(No.2020YFC2200901) （No.2020YFC2200901）
