  A sizable part of the fleet of heavy-duty machinery in the construction
equipment industry uses the conventional valve-controlled load-sensing
hydraulics. Rigorous climate actions towards reducing CO$_{2}$ emissions has
sparked the development of solutions to lower the energy consumption and
increase the productivity of the machines. One promising solution to having a
better balance between energy and performance is to build accurate models
(digital twins) of the real systems using data together with recent advances in
machine learning/model-based optimization to improve the control systems. With
a particular focus on real-world machines with multiple flow-controlled
actuators and shared variable-displacement pumps, this paper presents a
generalized machine learning approach to modeling the working pressure of the
actuators and the overall pump pressures. The procedures for deriving reaction
forces and flow rates as important input variables to the surrogate models are
described in detail. Using data from a real loader crane testbed, we
demonstrate training and validation of individual models, and showcase the
accuracy of pressure predictions in five different experiments under various
utilizations and pressure levels.

Systems and Control;Systems and Control