基于稀疏约束的最小方差无偏自适应荷载估计

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Abstract This paper presents an improved algorithm for load estimation based on sparse constraints, an improvement of the traditional minimum variance unbiased load estimation algorithm. The improvement is achieved by applying a sparse constraint on the force vector with the PM technique utilizing the space-sparse characteristic of the force to be estimated. This constraint transforms the unconstrained optimization of force estimation into a constrained optimization based on the l1-norm, effectively solving the force drift problem and improving the robustness of force estimation. Moreover, an adaptive estimation algorithm is introduced to realize the adaptive estimation of measurement noise covariance, as the conventional approach of manually setting the noise covariance is inconvenient in engineering applications. The performance of the proposed algorithm is evaluated through numerical simulations and experiments on a three-story shear building。

Key words： minimum variance unbiased estimation force estimation sparse constraint adaptive estimation force drift

Received: 14 December 2022 Published: 15 May 2024

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	LI Dongsheng1
	WEI Da2

Cite this article:

LI Dongsheng1,WEI Da2. Minimum variance unbiased adaptive load estimation based on sparse constraints[J]. JOURNAL OF VIBRATION AND SHOCK, 2024, 43(9): 158-165.

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http://jvs.sjtu.edu.cn/EN/ OR http://jvs.sjtu.edu.cn/EN/Y2024/V43/I9/158

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