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Given the problem that the low sampling period of the servo controller cannot provide high-frequency information for high-precision servo control system state recognition, this paper proposes a high-resolution signal reconstruction method based on sparse structure preservation. The servo system status data is reconstructed to obtain high sampling rate data equivalent to direct measurement, which provides support for extracting system features and status recognition. The main research content of this paper includes verifying the sparseness of the servo control signal, analyzing the consistency of the sparse structure of different sampling rates signals; extracting characteristics based on the combination of empirical mode decomposition (EMD) and principal component analysis (PCA) method. An adaptive sparse dictionary for servo control signals is trained by K-SVD. An objective function is constructed for high-resolution signal reconstruction based on the sparse structure retention properties. It is proved by simulations and experiments that the high-resolution reconstructed signal can be obtained, which is consistent with the high-resolution signal obtained by direct measurement. The method can be used as a reference for the analysis of low-sampling signals of servo control systems of industrial robots and similar equipment and has certain engineering application value.

High-Resolution Signal Reconstruction Method Based on Sparse Structure Preservation