Nonlinear model predictive control of a hydraulic excavator using Hammerstein models

Abstract

Hydraulic excavators play a crucial role on worldwide construction sites. Efficient operation of these machines therefore contributes to a quick completion of the construction task. In particular, optimized control strategies can lead to improvements with regard to machine performance, fuel consumption, and pollutant emissions. In this work, a nonlinear model of a hydraulic excavator is considered. It is shown that a simplified nonlinear model with Hammerstein structure can accurately represent the underlying dynamics for the purpose of control. Based on this model, a nonlinear model predictive control approach including an optimization algorithm is developed in order to have the excavator perform a task given through target positions of the four motion axes. Simulation results based on an application of the developed controller to a complex physical model of the excavator indicate good tracking performance and fast execution of the task.