Physics-informed online learning for velocity control of hydraulic excavators

Hydraulic excavators remain among the least automated construction machines due to nonlinear hydraulics, varying environment interactions, and strict real-time requirements. We present a physics-informed online learning framework that provides a standardized velocity-level control interface, abstracting machine-specific hydraulics and enabling integration with higher-level automation. A physics-motivated base model is augmented with an online residual learner to compensate nonlinearities in real time. Experiments on a 12-ton excavator in leveling, grading, and a demanding 4-point cycle show substantial improvements over the base model alone: maximum absolute tracking error is reduced by up to 63 % in leveling and 70 % in the 4-point cycle, while RMSE is reduced by up to 54 % and 69 %.