  Research on dynamics of robotic manipulators provides promising support for
model-based control. In general, rigorous first-principles-based dynamics
modeling and accurate identification of mechanism parameters are critical to
achieving high precision in model-based control, while data-driven model
reconstruction provides alternative approaches of the above process. Taking the
level of activation of data as an indicator, this paper classifies the
collected robotic manipulator data by means of K-means clustering algorithm.
With the fundamental prior knowledge, we find the corresponding dynamical
properties behind the classified data separately. Afterwards, the sparse
identification of nonlinear dynamics (SINDy) method is used to reconstruct the
dynamics model of the robotic manipulator step by step according to the
activation level of the classified data. The simulation results show that the
proposed method not only reduces the complexity of the basis function library,
enabling the application of SINDy method to multi-degree-of-freedom robotic
manipulators, but also decreases the influence of data noise on the regression
results. Finally, the dynamic control based on the reconfigured model is
deployed on the experimental platform, and the experimental results prove the
effectiveness of the proposed method.
