Hybrid Data-Driven Modelling for Inverse Control of Hydraulic Excavators
Jonas Weigand, Julian Raible, Nico Zantopp, Ozan Demir, Adrian Trachte, Achim Wagner, Martin Ruskowski
Abstract
We present a comprehensive comparison of hybrid Data-Driven Control (DDC) applied on a hydraulic excavator. DDC offers a state-of-the-art, high performance control based on data and expert knowledge. On the one hand, expert knowledge is complex to adapt to each unique excavator requiring substantial engineering efforts. On the other hand, purely data based control overcomes this drawback by adapting a Neural Network (NN) inverse control directly on measured input/output data. Yet, coverage of the entire phase space and extrapolation to unknown situations is challenging for solely data-driven approaches. On a real demonstrator, we analyze expert white box methods, solely data-driven black box approaches and a hybrid grey box approach which combines a data-driven and simplified expert model. We examine trajectory tracking performance, engineering effort and safe exploration as goal criteria. Besides various experiments for testing safety, we apply a Support-Vector-Machine (SVM) to analyze the extrapolation fitness of the data-driven components to unknown data.