Physics-Informed Neural Network for Model Prediction and Dynamics Parameter Identification of Collaborative Robot Joints

Collaborative robots have promising potential for widespread use in small-and-medium-sized enterprise (SME) manufacturing and production due to the development of increasingly sophisticated Human-Robot Collaboration technologies. However, predicting and identifying the behavior of collaborative robots remains a challenging problem due to the significant non-linear properties of their unique gearbox, the harmonic drive. To tackle the engineering problem, this work proposes a physics-informed neural network (PINN) to predict and identify collaborative robot joint dynamics. The procedure involves deriving the state-space dynamic model, embedding the system's dynamics into a recurrent neural network (RNN) with customized Runge-Kutta cells, obtaining labeled training data, predicting system responses, and estimating dynamic parameters. The proposed method is applied to predict and identify collaborative robot joint dynamics, and the results are verified and validated through numerical simulations and experimental testing, respectively. The obtained results demonstrate a high level of agreement with the ground truth and exhibit superior performance compared to the conventional PINN and the non-linear grey-box state-space estimation algorithm when confronted with non-linearity and dynamic coupling. Moreover, the PINN exhibits the potential for extension to various dynamic systems.