Real-time prediction of human-generated reference signals for advanced digging control*

In the realm of excavator control, advanced techniques, such as Model Predictive Control (MPC) and two-degrees-of-freedom structures (feedforward plus feedback), proved to have great potential for enhancing efficiency and performance. These methods rely on the knowledge of future reference, which is often pre-defined, to optimize the system behavior as a function of it. However, this assumption fails in applications where a human operator chooses the reference at runtime, such as in the case of non-autonomous digging operations. To cope with this problem, we study different approaches to use the collected data of human-generated reference signals to learn a predictive model of the operator commands. The considered methods are function approximation techniques based on Kriging, Set-Membership, and LSTM Neural Networks. We summarize the principles and the implementation of each method, and compare their performance using an experimental data-set of operations from a real-world excavator, where four operator-defined reference signals are predicted.