Tracking Error Prediction and Compensation of Industrial Robot Considering Dynamical Factors via Physics-Informed Machine Learning

Tracking error prediction and compensation is essential to ensure the accuracy of robotic motion trajectories in various tasks. Due to the structural characteristics of serial robots, the tracking error is influenced by pose-dependent dynamics. To enhance the tracking control accuracy of robots, this study proposes a physics-informed machine learning (PIML)-assisted method for tracking error prediction and compensation. This method explicitly considers the influence of pose-dependent robotic dynamics and the characteristics of the closed-loop control mechanism through a dynamics-embedded neural network. Specifically, the theoretical current is calculated as a critical input for the neural network model, providing a physical constraint during the parameter update process. Moreover, a current-error dual-loop architecture is designed to help the neural network capture the mutual influence between the control current and tracking error within the robotic closed-loop control mechanism. Finally, the errors predicted by the proposed method are utilized for trajectory precompensation. The PIML-assisted tracking error prediction and compensation approach effectively combines the strengths of mechanistic and data-driven models, offering a robust solution for ensuring high-accuracy robotic tracking control. Experimental results demonstrate that the proposed approach achieves a 19.04% reduction in prediction error and significantly reduces tracking error by 74.74%, which is 4.25% greater than the improvement obtained by normal ML-based compensation methods.