This study addresses the control challenges involved in automating an excavator's operation for loading soil onto a dump truck. For an efficient loading motion, the tip path should be minimized to avoid contacting the truck. It is also crucial to prevent soil from spilling out of the bucket during the loading process. Model Predictive Control (MPC) is well-suited for managing hydraulic excavators under these multiple performance criteria. However, obtaining accurate models of hydraulic actuator dynamics is challenging because they vary significantly with factors such as oil temperature and excavator posture. When accurate models are not available, MPC may not deliver adequate control performance. To address this issue, we propose a method that compensates for discrepancies between the actual actuator dynamics and the internal model assumed in MPC by integrating actuator compensators. These compensators are tuned in real time using a data-driven control approach. An additional advantage of this method is that the MPC, which is tuned based on a nominal model, continues to perform effectively even when the actuator dynamics change. The effectiveness of the proposed method was validated through numerical simulations.
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