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Impact Force Localization and Reconstruction via ADMM-based sparse regularization Method

Chinese Journal of Mechanical Engineering 2024年第3期37卷 170-188页

作者： Yanan Wang Lin Chen Junjiang Liu Baijie Qiao Weifeng He Xuefeng Chen National Key Lab of Aerospace Power System and Plasma Technology Xi’an Jiaotong UniversityXi’an 710049China School of Mechanical Engineering Xi’an Jiaotong UniversityXi’an 710049China

In practice,simultaneous impact localization and time history reconstruction can hardly be achieved,due to the illposed and under-determined problems induced by the constrained and harsh measuring *** l_(1) regularization can be used to obtain sparse solutions,it tends to underestimate solution amplitudes as a biased *** address this issue,a novel impact force identification method with l_(p) regularization is proposed in this paper,using the alternating direction method of multipliers(ADMM).By decomposing the complex primal problem into sub-problems solvable in parallel via proximal operators,ADMM can address the challenge *** mitigate the sensitivity to regularization parameters,an adaptive regularization parameter is derived based on the K-sparsity ***,an ADMM-based sparse regularization method is developed,which is capable of handling l_(p) regularization with arbitrary p values using adaptively-updated *** effectiveness and performance of the proposed method are validated on an aircraft skin-like composite ***,an investigation into the optimal p value for achieving high-accuracy solutions via l_(p) regularization is *** turns out that l_(0.6)regularization consistently yields sparser and more accurate solutions for impact force identification compared to the classic l_(1) regularization *** impact force identification method proposed in this paper can simultaneously reconstruct impact time history with high accuracy and accurately localize the impact using an under-determined sensor configuration.

关键词： Impact force identification non-convex sparse regularization Alternating direction method of multipliers Proximal operators

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