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• W4313157829 abstract "Robot dynamic modeling and parameter identification are essential for many analyses. High-fidelity multi-body dynamics simulators can model the robot’s dynamic behavior, but they can’t identify the robot non-linear dynamic model, which is needed for controller design. This study proposes a three-step machine learning framework for obtaining the dynamic equations of serial manipulators from data. This framework consists of three steps. Initially, A library of candidate functions is first constructed, together with a data set based on robot unforced response. Secondly, the best models that can represent dynamic systems for each candidate function utilizing the training data set are then obtained using the SINDy-PI algorithm and Akaike Information Criterion (AIC). Through the MSE and test data, those best models will be reduced to get the functions that best describe the dynamic system. Finally, the dynamic equations that characterize the system are derived using the SINDy algorithm, including the applied force or torque. The technique was tested on three case studies —double pendulum, two-link of KUKA robot, and two-link of Stanford robot. The framework correctly determines the structure of the dynamic system and simultaneously accurately identified its parameters. The framework was able to deal with an ill-conditioned system of equations that arises for complex robot configuration." @default.
• W4313157829 created "2023-01-06" @default.
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• W4313157829 date "2022-01-01" @default.
• W4313157829 modified "2023-10-16" @default.
• W4313157829 title "A Framework for Data Driven Dynamic Modeling of Serial Manipulators" @default.
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• W4313157829 doi "https://doi.org/10.1109/access.2022.3223182" @default.
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