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A sizable part of the fleet of heavy-duty machinery in the constructionequipment industry uses the conventional valve-controlled load-sensinghydraulics. Rigorous climate actions towards reducing CO$_{2}$ emissions hassparked the development of solutions to lower the energy consumption andincrease the productivity of the machines. One promising solution to having abetter balance between energy and performance is to build accurate models(digital twins) of the real systems using data together with recent advances inmachine learning/model-based optimization to improve the control systems. Witha particular focus on real-world machines with multiple flow-controlledactuators and shared variable-displacement pumps, this paper presents ageneralized machine learning approach to modeling the working pressure of theactuators and the overall pump pressures. The procedures for deriving reactionforces and flow rates as important input variables to the surrogate models aredescribed in detail. Using data from a real loader crane testbed, wedemonstrate training and validation of individual models, and showcase theaccuracy of pressure predictions in five different experiments under variousutilizations and pressure levels.

Towards Energy Efficient Control for Commercial Heavy-Duty Mobile Cranes: Modeling Hydraulic Pressures using Machine Learning