Positioning reliability estimation of SDOF manipulator based on deep learning

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Journal of Vibration and Shock ›› 2021, Vol. 40 ›› Issue (15) : 246-252.

BAO Dan, HOU Baolin

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Abstract

Here, aiming at multiple parameters with interval uncertainty existing in a SDOF manipulator system and the system being subjected to effects of external random disturbance during operation, a method based on deep learning neural network model was proposed to estimate positioning reliability of the manipulator. Firstly, the uncertainty analysis was performed for the system, and uncertain parameters in the system operation process were described using interval number to establish the uncertain model of the SDOF manipulator. Interval uncertain parameters could be regarded as fixed values in process of one action. Parameters of several sets of experiments were identified through combining experiment data and the parametric identification method, and the correctness of the simulation model of SDOF manipulator was verified. Secondly, Latin hypercube sampling was done for interval uncertain parameters and the sampled data were brought into the simulation model. The manipulator could realize positioning in external random disturbance to obtain sample data for training the neural network model, construct the neural network model based on levenberg marquardt (LM) algorithm, and perform Monte Carlo simulation analysis. It was shown that the positioning reliability of the SDOF manipulator is estimated as 84.12%. Finally, analyzing several sets of experimental data showed that the proposed method can have high efficiency and effectiveness, and provide a new idea for positioning reliability estimation of other nonlinear complex systems.

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manipulator / deep learning / positioning reliability / parametric identification / random disturbance

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BAO Dan, HOU Baolin. Positioning reliability estimation of SDOF manipulator based on deep learning[J]. Journal of Vibration and Shock, 2021, 40(15): 246-252

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